Diseases can be of two types
Diseases that spread from one person to another are called communicable diseases. They are usually caused by microorganisms called pathogens (fungi, rickettsia, bacteria, viruses, protozoans, and worms). When an infected person discharges bodily fluids, pathogens may exit the host and infect a new person (sneezing, coughing etc). Examples include Cholera, chickenpox, malaria etc.
These diseases are caused by pathogens, but other factors such as age, nutritional deficiency, gender of an individual, and lifestyle also influence the disease. Examples include hypertension , diabetes, and cancer. They do not spread to others and they restrain within a person who has contracted them. Alzheimer’s disease, asthma, cataract and heart diseases are other non-infectious diseases.
Read more: Infectious diseases
Based on these above classifications, a disease may fall into any number of these classifications.
They are mainly caused by the malfunctioning of vital organs in the body due to the deterioration of cells over time. Diseases such as osteoporosis show characteristics of degenerative diseases in the form of increased bone weakness. This increases the risk of bone fractures.
When degeneration happens to the cells of the central nervous system, such as neurons, the condition is termed as a neurodegenerative disorder. Alzheimer’s is a prominent example of this disorder. Degenerative diseases are usually caused by ageing and bodywear. Others are caused by lifestyle choices and some are hereditary.
An allergic reaction arises when the body becomes hypersensitive to certain foreign substances called allergens. This usually happens when the immune system reacts abnormally to any seemingly harmless substances. Common allergens include dust, pollen, animal dander, mites, feathers, latex and also certain food products like nuts and gluten. Peanuts and other nuts have the capability to cause severe allergic reactions that may induce life-threatening conditions such as difficulty in breathing, tissues swelling up and blocking the airways and anaphylaxis shock.
Other common and less life-threatening symptoms include coughing, sneezing, running nose, itchy and red eyes, and skin rashes. One of the best examples of this allergic reaction is asthma . Sometimes, bee stings and ant bites also trigger allergies. Consumption of shellfish and certain medications can induce allergic reactions.
Asthma is a chronic disease, that mainly affects the bronchi and bronchioles of the lungs. One of the factors responsible for this is airborne allergens such as pollens or dust. Symptoms include difficulty in breathing, wheezing, and coughing.
They occur due to the deficiencies of hormones, minerals, nutrients, and vitamins. For example, diabetes occurs due to an inability to produce or utilize insulin, goitre is mainly caused by iodine deficiency, and kwashiorkor is caused by a lack of proteins in the diet. Vitamin B1 deficiency causes beriberi.
Read more: Deficiency Diseases
It is an abnormal enlargement of the thyroid gland by blocking the oesophagus or other organs of the chest and neck. This causes difficulty in breathing and eating.
Blood contains plasma, white blood cells, platelets and red blood cells. When any of these components are affected, it can lead to blood disorders. For instance, red blood cells are destroyed when a person contracts sickle cell disease. The red blood cells are distorted into the shape of a sickle (hence, the name) and they lose their ability to carry oxygen. Consequently, this disease is characterized by symptoms similar to chronic anaemia, such as shortness of breath and tiredness.
Other diseases such as eosinophilic disorders, leukaemia, myeloma (cancer of plasma cells in bone marrow), Sickle Cell Anemia, Aplastic Anemia, Hemochromatosis and Von Miller and Disease (blood-clotting disorder) fall under this classification.
General Symptoms: Pale skin, swelling of lymph nodes, fever, bleeding, bruising, skin rashes, etc.
We have seen the classification of different entities based on various characteristics, for simplification, we classify organisms to group them together and study about them as a class. Similarly, diseases are caused by different microorganisms and can be classified as diseases caused by bacteria, fungi, viruses etc. Some diseases are also caused by multicellular organisms such as worms.
Listed below are a few diseases and the disease-causing agents
Plague | Pasteurella pestis |
Cholera | Vibrio comma (Vibrio cholera) |
Tetanus | Clostridium tetani |
Anthrax | Bacillus anthracis |
Whooping cough | Bordetella pertussis |
Human papillomavirus infection | Human papillomavirus |
Acquired Immune Deficiency Syndrome (AIDS) | Human Immunodeficiency Virus (HIV) |
Hepatitis | Hepatitis A, Hepatitis B, Hepatitis C, Hepatitis D, Hepatitis E viruses |
Chickenpox | Varicella-zoster virus (VZV) |
Meningoencephalitis | Naegleria fowleri (amoeba) |
Also Read: Harmful Microorganisms
To know more about human diseases, their types, causes, symptoms and other related topics, keep visiting BYJU’S Biology.
What is meant by communicable diseases name any two communicable diseases., what are genetic diseases, what are the disease-causing organisms known as, name the disease caused by the deficiency of insulin hormone in the body., what is the difference between an infectious disease and a communicable disease.
Thus, All infectious diseases are not communicable but all communicable diseases are infectious.
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A fter a lifetime in the field of epigenetics, and nearly 20 years after my colleagues and I coined the term “genome editing,” I will be the first to admit that describing the “ epi genome”—a marvelous biological process that guides what our genes do—takes a bit of explaining. I find that thinking about the genome and epigenome in terms of music and sound-mixing can be helpful here. We experience all sorts of music as we go through life, from Bach and Brahms to Laufey and Lizzo. It is remarkable that you can do so many different things musically from just a few basic components. You have a defined set of notes , which can be played separately or together in an enormous number of combinations and time signatures. Those notes can be played at different volumes —some louder, some softer. And finally, those same notes can have different textures . The note of A as played on a violin sounds very different when played by a distorted, death-metal guitar. Each has the same number of vibrations per unit time, but our experience of them is not the same at all.
So now, let’s turn to genes. Humans have around 20,000 of them—which is not many more than the total number of genes in a fruit fly. Initial estimates were far higher, at around 120,000, because we thought that more genes are needed to make more complicated organisms. Our thinking was wrong. The wondrous complexity we observe emerges out of combinations of genes, functioning with a certain order and timing—rather than all of your genes doing “everything, everywhere, all at once.” Our body consists of several hundred different cell types (red blood cell, skin fibroblast, neuron), and about 8,000 genes in a given human cell come together, each at a specific volume, timing, and texture to bring it to life. Scientists call this gene “expression”—a term aptly borrowed from the arts. What coordinates it? Consider a music score for a song—whether it’s Taylor Swift’s Love Story or Schubert’s Message of Love . In both, you would see notes on a musical stave, with specific markings for rhythm, volume, and pitch. For our genome, every four bars of music would be accompanied by four pages of guidance on how to play them correctly. Scientists have discovered that in addition to 20,000 genes, our genome contains about 3 million sets of instructions on how to express them (akin to dials on a soundboard); together, these cover a quarter of human DNA. Our body contains about 40 trillion cells and in each one the genes are expressing themselves in a distinct way (a blood cell makes different proteins than a liver cell than a lung cell). And now think of setting each dial on a soundboard to the exact position you need for the music to sound like Swift or like Schubert. The epigenome is the total set of “dial settings” for all the genes that are expressed in a given human cell and give it its biological identity.
On a fundamental level, health (or the lack of it) is created and maintained through a kind of epigenomically-mediated harmony. If you’ve ever heard a beginning violinist working through a simple piece, you’ll know it's sonically grating. We hear the lack of harmony, and it's painful.
But what is lack of harmony? It could simply be the wrong note played at the wrong time. But it can also arise from a failure of coordination. Any time you have more than one instrument on stage, if they're not coherent (you might picture a middle-school orchestra, here), it just hurts.
Music can also be ruined when the relative volumes within it are wrong. (“Why are the drums so loud in this mix? We need less vocal and more bass!”) In music, it seems intuitive to us that the various notes and instruments must come together at the correct volumes, and in the right rhythm. And so it is with gene expression in relation to health and disease.
Read More: The Gene-Editing Revolution Is Already Here
We have had access to the complete, human genome sequence since 2003 . Our DNA is long; reading one letter of human DNA per second, it would take you a century to read the whole genome. Think 500 textbooks, stacked one atop the other. Now imagine reading different versions of that text—each unique to different people and populations, and asking: where are the genes that make us sick? Where are the genes that lead to heart attacks, or irritable bowel syndrome? As it turns out, many of the genetic signatures that cause us to develop such common and degenerative conditions are not actually located inside the genes themselves .
Now if that statement inspires some confusion, then you are not alone. We scientists were just as confused at first. But what we've essentially figured out is this: Few of the common diseases we suffer from—be they cardiovascular, autoimmune, or neurodegenerative—are the direct result of broken or defective genes (or keeping within our musical metaphor, broken or defective instruments). The pianos are fine. The guitars are fine. They are simply being played in the wrong way.
Soon after the first sequence of the human genome was determined, scientists started a large effort to compare DNA between individuals with and without certain diseases. This approach—called a Genome-Wide Association Study (or GWAS) has been applied thousands of times for every imaginable human trait difference, including whether someone is a “morning person” or whether a given individual is likely to get celiac disease. What these studies found was this: susceptibility to practically every, major, non-infectious disease rarely lies in the genetic “notes” themselves. Rather, about 90% of it lies in the instructions of how to play those notes .
Armed with this knowledge—and empowered by the development of CRISPR-based proteins that can edit both genome and epigenome—scientists across academia and industry have been racing toward the goal of a new class of genetic medicines . Medicines that can help patients retune the ill-timed notes or imbalanced volumes leading to disease.
The basic idea is this: if discord of gene expression leads to disease, could we not simply re-tune this orchestra of gene output to restore harmony in health?
We have a strong “yes” as an answer in the recent development of a cure for sickle cell disease . This medicine does not involve repairing the mutation that causes the disease –a mutation that breaks a gene that makes oxygen-carrying hemoglobin in our red blood cells. Instead, guided by a GWAS for genetic variants that protect against this disease, scientists figured out how to "wake up” a gene called fetal hemoglobin that normally goes silent after birth. In this work—a collaboration between Dr. Stuart Orkin, scientists at the University of Washington, and a group led by myself—altering one of the epigenetic switches in the “symphony” of how our body makes hemoglobin restored health to sickle red blood cells. In fact, to date, this has helped over 50 persons living with sickle cell disease!
Where this really gets exciting is that there are a vast number of diseases like this— in which otherwise healthy genes are being played at the wrong volume , at the wrong time , or in the wrong combinations. For each of these, we can move the sliders to shift the timing, volume, and texture of what each individual gene “sounds” like. And critically, we can do it without having to rewrite the music.
The emergence of this new transformative therapeutic power begs the question: what are the areas of most need, and how could we put this to the best, possible use? It makes sense for the first focus to be on severe disease. Take, for example, someone with devastatingly high cholesterol, at a high risk of early death from cardiovascular disease. If they do not respond to the usual medications, what are we to do? And what of chronic viral infections like Hepatitis B, for which there are treatments, but no effective cure? What options are there for those who face a lifetime of liver disease, a high risk of liver cancer, and no long-term prospects beyond liver transplant?
Read More: How Gene Editing Could Help Solve the Problem of Poor Cholesterol
Could we just remove a gene, rip out the page wholesale? Yes. I'm a gene editor, and I firmly believe that gene editing has the potential to cure hundreds, if not thousands of rare, single-gene diseases—a potential recently demonstrated in several clinical trials. But I will be the first to admit that once you have gene-edited somebody, you’re done. There is no way back—you are gene-edited for life. If you are facing an otherwise untreatable condition, you might be fine with that. But for those with partly manageable conditions like high cholesterol or chronic viral infection, there may be less enthusiasm in some folks for this all-or-nothing approach.
With gene tuning, we can place an X mark over a precise part of the music score and say, “Don’t play this.” You can turn this gene up, turn that gene down, and observe the hoped-for benefit. And if something goes awry you can – at least in principle – reverse the effects. Besides the potential benefit of reversibility, gene tuning also offers control over duration of effect. For example, a remarkable new wave of cancer treatments has emerged recently from work by physicians and scientists at the University of Pennsylvania in which the patient’s own immune system cells are reprogrammed to attack a blood cancer. Once they have done their job, you may want those same cells to calm down and return to standby mode—lest they cause collateral damage through their persistent (albeit well-intentioned) hyperactivity. Gene tuning is built for precisely this kind of nuance, allowing you to raise or lower the volume of one or more genes gradually, or for a fixed, desired duration of time.
Beyond this, there is another reason why gene tuning could transform the application of genetic medicine. Put simply: tweaking and editing single genes will only get you so far.
The majority of common and chronic diseases involve expression changes in multiple genes. To tackle these, we will have to re-tune not just one instrument, but a whole section of the orchestra.
Take the case of chronic, age-related autoimmune conditions. Imagine retuning multiple, immune-system genes to turn “attack yourself” music into “protect yourself” music. We actually know which genes to tune for that. And the ability to set their volume gauges to zero, to 10,000, or anywhere in between is ultimately where the next generation of therapeutics are headed. For the vast majority of disease, we don't need the genes to be off, we just need them to start performing at the right volume. Same tune, only less cowbell. Looking ahead at the next decade, I see it as an important window of opportunity for gene tuning to go after diseases where multiple levers need to be adjusted on the soundboard—and where the adjustment level needs to be a graded one, rather than all-or-none. This is not to say that gene editing cannot do similar things. But sometimes you just need to match the problem to the solution best configured to solve it. You could play the intro to the Jaws theme on a flute – but it will sound better on a double bass. So when can we expect these gene-tuning therapeutics to actually become available to patients? In a time where genetic therapy for cancer is becoming the standard of care , and where we have an approved CRISPR medicine for the most common genetic disease on earth, we are closer than ever. The number one thing we have learned from that history is every new technology stands on the shoulders of previous ones,
The first clinical trials for gene tuning are likely to happen very soon - perhaps within the year. Encouraged by the exponential growth in the broader gene therapy space, many academic scientists and biotech companies are working hard to bring therapeutic gene tuning to patients. Clinicians and regulators worldwide have learned to appreciate the power and potential of gene editing, and I am hopeful we will see a similar phenomenon for gene tuning as well. The second half of John Lennon’s classic Strawberry Fields Forever was sped up by the Beatles’ producer, George Martin, to sound right—one of countless examples in the history of music where small tweaks to the score made a big difference. Gene tuning is just getting started on a similar journey to bring harmony to human health—a big challenge, to be sure, but one, I sincerely hope, we can work out.
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A review of computer vision-based crack detection methods in civil infrastructure: progress and challenges.
2. crack detection combining traditional image processing methods and deep learning, 2.1. crack detection based on image edge detection and deep learning, 2.2. crack detection based on threshold segmentation and deep learning, 2.3. crack detection based on morphological operations and deep learning, 3. crack detection based on multimodal data fusion, 3.1. multi-sensor fusion, 3.2. multi-source data fusion, 4. crack detection based on image semantic understanding, 4.1. crack detection based on classification networks, 4.2. crack detection based on object detection networks, 4.3. crack detection based on segmentation networks.
Model | Improvement/Innovation | Backbone/Feature Extraction Architecture | Efficiency | Results |
---|---|---|---|---|
FCS-Net [ ] | Integrating ResNet-50, ASPP, and BN | ResNet-50 | - | MIoU = 74.08% |
FCN-SFW [ ] | Combining fully convolutional network (FCN) and structural forests with wavelet transform (SFW) for detecting tiny cracks | FCN | Computing time = 1.5826 s | Precision = 64.1% Recall = 87.22% F1 score = 68.28% |
AFFNet [ ] | Using ResNet101 as the backbone network, and incorporating two attention mechanism modules, namely VH-CAM and ECAUM | ResNet101 | Execution time = 52 ms | MIoU = 84.49% FWIoU = 97.07% PA = 98.36% MPA = 92.01% |
DeepLabv3+ [ ] | Replacing ordinary convolution with separable convolution; improved SE_ASSP module | Xception-65 | - | AP = 97.63% MAP = 95.58% MIoU = 81.87% |
U-Net [ ] | The parameters were optimized (the depths of the network, the choice of activation functions, the selection of loss functions, and the data augmentation) | Encoder and decoder | Analysis speed (1024 × 1024 pixels) = 0.022 s | Precision = 84.6% Recall = 72.5% F1 score = 78.1% IoU = 64% |
KTCAM-Net [ ] | Combined CAM and RCM; integrating classification network and segmentation network | DeepLabv3 | FPS = 28 | Accuracy = 97.26% Precision = 68.9% Recall = 83.7% F1 score = 75.4% MIoU = 74.3% |
ADDU-Net [ ] | Featuring asymmetric dual decoders and dual attention mechanisms | Encoder and decoder | FPS = 35 | Precision = 68.9% Recall = 83.7% F1 score = 75.4% MIoU = 74.3% |
CGTr-Net [ ] | Optimized CG-Trans, TCFF, and hybrid loss functions | CG-Trans | - | Precision = 88.8% Recall = 88.3% F1 score = 88.6% MIoU = 89.4% |
PCSN [ ] | Using Adadelta as the optimizer and categorical cross-entropy as the loss function for the network | SegNet | Inference time = 0.12 s | mAP = 83% Accuracy = 90% Recall = 50% |
DEHF-Net [ ] | Introducing dual-branch encoder unit, feature fusion scheme, edge refinement module, and multi-scale feature fusion module | Dual-branch encoder unit | - | Precision = 86.3% Recall = 92.4% Dice score = 78.7% mIoU = 81.6% |
Student model + teacher model [ ] | Proposed a semi-supervised semantic segmentation network | EfficientUNet | - | Precision = 84.98% Recall = 84.38% F1 score = 83.15% |
6. evaluation index, 7. discussion, 8. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.
Aspect | Combining Traditional Image Processing Methods and Deep Learning | Multimodal Data Fusion |
---|---|---|
Processing speed | Moderate—traditional methods are usually fast, but deep learning models may be slower, and the overall speed depends on the complexity of the deep learning model | Slower—data fusion and processing speed can be slow, especially with large-scale multimodal data, involving significant computational and data transfer overhead |
Accuracy | High—combines the interpretability of traditional methods with the complex pattern handling of deep learning, generally resulting in high detection accuracy | Typically higher—combining different data sources (e.g., images, text, audio) provides comprehensive information, improving overall detection accuracy |
Robustness | Strong—traditional methods provide background knowledge, enhancing robustness, but deep learning’s risk of overfitting may reduce robustness | Very strong—fusion of multiple data sources enhances the model’s adaptability to different environments and conditions, better handling noise and anomalies |
Complexity | High—integrating traditional methods and deep learning involves complex design and balancing, with challenges in tuning and interpreting deep learning models | High—involves complex data preprocessing, alignment, and fusion, handling inconsistencies and complexities from multiple data sources |
Adaptability | Strong—can adapt to different types of cracks and background variations, with deep learning models learning features from data, though it requires substantial labeled data | Very strong—combines diverse data sources, adapting well to various environments and conditions, and handling complex backgrounds and variations effectively |
Interpretability | Higher—traditional methods provide clear explanations, while deep learning models often lack interpretability; combining them can improve overall interpretability | Lower—fusion models generally have lower interpretability, making it difficult to intuitively explain how different data sources influence the final results |
Data requirements | High—deep learning models require a lot of labeled data, while traditional methods are more lenient, though deep learning still demands substantial data | Very high—requires large amounts of data from various modalities, and these data need to be processed and aligned effectively for successful fusion |
Flexibility | Moderate—combining traditional methods and deep learning handles various types of cracks, but may be limited in very complex scenarios | High—handles multiple data sources and different crack information, improving performance in diverse conditions through multimodal fusion |
Real-time capability | Poor—deep learning models are often slow to train and infer, making them less suitable for real-time detection, though combining with traditional methods can help | Poor—multimodal data fusion processing is generally slow, making it less suitable for real-time applications |
Maintenance cost | Moderate to high—deep learning models require regular updates and maintenance, while traditional methods have lower maintenance costs | High—involves ongoing maintenance and updates for multiple data sources, with complex data preprocessing and fusion processes |
Noise handling | Good—traditional methods effectively handle noise under certain conditions, and deep learning models can mitigate noise effects through training | Strong—multimodal fusion can complement information from different sources, improving robustness to noise and enhancing detection accuracy |
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Method | Features | Domain | Dataset | Image Device/Source | Results | Limitations |
---|---|---|---|---|---|---|
Canny and YOLOv4 [ ] | Crack detection and measurement | Bridges | 1463 images 256 × 256 pixels | Smartphone and DJI UAV | Accuracy = 92% mAP = 92% | The Canny edge detector is affected by the threshold |
Canny and GM-ResNet [ ] | Crack detection, measurement, and classification | Road | 522 images 224 × 224 pixels | Concrete crack sub-dataset | Precision = 97.9% Recall = 98.9% F1 measure = 98.0% Accuracy in shadow conditions = 99.3% Accuracy in shadow-free conditions = 99.9% | Its detection performance for complex cracks is not yet perfect |
Sobel and ResNet50 [ ] | Crack detection | Concrete | 4500 images 100 × 100 pixels | FLIR E8 | Precision = 98.4% Recall = 88.7% F1 measure = 93.2% | - |
Sobel and BARNet [ ] | Crack detection and localization | Road | 206 images 800 × 600 pixels | CrackTree200 dataset | AIU = 19.85% ODS = 79.9% OIS = 81.4% | Hyperparameter tuning is needed to balance the penalty weights for different types of cracks |
Canny and DeepLabV3+ [ ] | Crack detection | Road | 2000 × 1500 pixels | Crack500 dataset | MIoU = 77.64% MAE = 1.55 PA = 97.38% F1 score = 63% | Detection performance deteriorating in dark environments or when interfering objects are present |
Canny and RetinaNet [ ] | Crack detection and measurement | Road | 850 images 256 × 256 pixels | SDNET 2018 dataset | Precision = 85.96% Recall = 84.48% F1 score = 85.21% | - |
Canny and Transformer [ ] | Crack detection and segmentation | Buildings | 11298 images 450 × 450 pixels | UAVs | GA = 83.5% MIoU = 76.2% Precision = 74.3% Recall = 75.2% F1 score = 74.7% | Resulting in a marginal increment in computational costs for various network backbones |
Canny and Inception-ResNet-v2 [ ] | Crack detection, measurement, and classification | High-speed railway | 4650 images 400 × 400 pixels | The track inspection vehicle | High severity level: Precision = 98.37% Recall = 93.82% F1 score = 95.99% Low severity level: Precision = 94.25% Recall = 98.39% F1 score = 96.23% | Only the average width was used to define the severity of the crack, and the influence of the length on the detection result was not considered |
Canny and Unet [ ] | Crack detection | Buildings | 165 images | - | SSIM = 14.5392 PSNR = 0.3206 RMSE = 0.0747 | Relies on a large amount of mural data for training and enhancement |
Method | Features | Domain | Dataset | Image Device/Source | Results | Limitations |
---|---|---|---|---|---|---|
Otsu and Keras classifier [ ] | Crack detection, measurement, and classification | Concrete | 4000 images 227 × 227 pixels | Open dataset available | Classifiers accuracy = 98.25%, 97.18%, 96.17% Length error = 1.5% Width error = 5% Angle of orientation error = 2% | Only accurately quantify one single crack per image |
Otsu and TL MobileNetV2 [ ] | Crack detection, measurement, and classification | Concrete | 11435 images 224 × 224 pixels | Mendeley data—crack detection | Accuracy = 99.87% Recall = 99.74% Precision = 100% F1 score = 99.87% | Dependency on image quality |
Otsu, YOLOv7, Poisson noise, and bilateral filtering [ ] | Crack detection and classification | Bridges | 500 images 640 × 640 pixels | Dataset | Training time = 35 min Inference time = 8.9 s Target correct rate = 85.97% Negative sample misclassification rate = 42.86% | It does not provide quantified information such as length and area |
Adaptive threshold and WSIS [ ] | Crack detection | Road | 320 images 3024 × 4032 pixels | Photos of cracks | Recall = 90% Precision = 52% IoU = 50% F1 score = 66% Accuracy = 98% | For some small cracks (with a width of less than 3 pixels), model can only identify the existence of small cracks, but it is difficult to depict the cracks in detail |
Adaptive threshold and U-GAT-IT [ ] | Crack detection | Road | 300 training images and237 test images | DeepCrack dataset | Recall = 79.3% Precision = 82.2% F1 score = 80.7% | Further research is needed to address the interference caused by factors such as small cracks, road shadows, and water stains |
Local thresholding and DCNN [ ] | Crack detection | Concrete | 125 images 227 × 227 pixels | Cameras | Accuracy = 93% Recall = 91% Precision = 92% F1 score = 91% | - |
Otsu and Faster R-CNN [ ] | Crack detection, localization, and quantification | Concrete | 100 images 1920 × 1080 pixels | Nikon d7200 camera and Galaxy s9 camera | AP = 95% mIoU = 83% RMSE = 2.6 pixels Length accuracy = 93% | The proposed method is useful for concrete cracks only; its applicability for the detection of other crack materials might be limited |
Adaptive Dynamic Thresholding Module (ADTM) and Mask DINO [ ] | Crack detection and segmentation | Road | 395 images 2000 × 1500 pixels | Crack500 | mIoU = 81.3% mAcc = 96.4% gAcc = 85.0% | ADTM module can only handle binary classification problems |
Dynamic Thresholding Branch and DeepCrack [ ] | Crack detection and classification | Bridges | 3648 × 5472 pixels | Crack500 | mIoU = 79.3% mAcc = 98.5% gAcc = 86.6% | Image-level thresholds lead to misclassification of the background |
Method | Features | Domain | Dataset | Image Device/Source | Results | Limitations |
---|---|---|---|---|---|---|
Morphological closing operations and Mask R-CNN [ ] | Crack detection | Tunnel | 761 images 227 × 227 pixels | MTI-200a | Balanced accuracy = 81.94% F1 score = 68.68% IoU = 52.72% | Relatively small compared to the needs of the required sample size for universal conditions |
Morphological operations and Parallel ResNet [ ] | Crack detection and measurement | Road | 206 images (CrackTree200) 800 × 600 pixels and 118 images (CFD) 320 × 480 pixels | CrackTree200 dataset and CFD dataset | CrackTree200: Precision = 94.27% Recall = 92.52% F1 = 93.08% CFD: Precision = 96.21% Recall = 95.12% F1 = 95.63% | The method was only performed on accurate static images |
Closing and CNN [ ] | Crack detection, measurement, and classification | Concrete | 3208 images 256 × 256 pixels or 128 × 128 pixels | Hand-held DSLR cameras | Relative error = 5% Accuracy > 95% Loss < 0.1 | The extraction of the cracks’ edge will have a larger influence on the results |
Dilation and TunnelURes [ ] | Crack detection, measurement, and classification | Tunnel | 6810 images image sizes vary 10441 × 2910 to 50739 × 3140 | Night 4K line-scan cameras | AUC = 0.97 PA = 0.928 IoU = 0.847 | The medial-axis skeletonization algorithm created many errors because it was susceptible to the crack intersection and the image edges where the crack’s representation changed |
Opening, closing, and U-Net [ ] | Crack detection, measurement, and classification | Concrete | 200 images 512 × 512 pixels | Canon SX510 HS camera | Precision = 96.52% Recall = 93.73% F measure = 96.12% Accuracy = 99.74% IoU = 78.12% | It can only detect the other type of cracks which have the same crack geometry as that of thermal cracks |
Morphological operations and DeepLabV3+ [ ] | Crack detection and measurement | Masonry structure | 200 images 780 × 355 pixels and 2880 × 1920 pixels | Internet, drones, and smartphones | IoU = 0.97 F1 score = 98% Accuracy = 98% | The model will not detect crack features that do not appear in the dataset (complicated cracks, tiny cracks, etc.) |
Erosion, texture analysis techniques, and InceptionV3 [ ] | Crack detection and classification | Bridges | 1706 images 256 × 256 pixels | Cameras | F1 score = 93.7% Accuracy = 94.07% | - |
U-Net, opening, and closing operations [ ] | Crack detection and segmentation | Bridges | 244 images 512 × 512 pixels | Cameras | mP = 44.57% mR = 53.13% Mf1 = 42.79% mIoU = 64.79% | The model lacks generality, and there are cases of false detection |
Sensor Type | Fusion Method | Advantages | Disadvantages | Application Scenarios |
---|---|---|---|---|
Optical sensor [ ] | Data-level fusion | High resolution, rich in details | Susceptible to light and occlusion | Surface crack detection, general environments |
Thermal sensor [ ] | Feature level fusion | Suitable for nighttime or low-light environments, detects temperature changes | Low resolution, lack of detail | Nighttime detection, heat-sensitive areas, large-area surface crack detection |
Laser sensor [ ] | Data-level fusion and feature level fusion | High-precision 3D point cloud data, accurately measures crack morphology | High equipment cost, complex data processing | Complex structures, precise measurements |
Strain sensor [ ] | Feature level fusion and decision-level fusion | High sensitivity to structural changes; durable | Requires contact with the material; installation complexity | Monitoring structural health in bridges and buildings; detecting early-stage crack development |
Ultrasonic sensor [ ] | Data-level fusion and feature level fusion | Detects internal cracks in materials, strong penetration | Affected by material and geometric shape, limited resolution | Internal cracks, metal material detection |
Optical fiber sensor [ ] | Feature level fusion | High sensitivity to changes in material properties, non-contact measurement | Affected by environmental conditions, requires calibration | Surface crack detection, structural health monitoring |
Vibration sensor [ ] | Data-level fusion | Detects structural vibration characteristics, strong adaptability | Affected by environmental vibrations, requires complex signal processing | Dynamic crack monitoring, bridges and other structures |
Multispectral satellite sensor [ ] | Data-level fusion | Rich spectral information | Limited spectral resolution, weather- and lighting-dependent, high cost | Pavement crack detection, bridge and infrastructure monitoring, building facade inspection |
High-resolution satellite sensors [ ] | Data-level fusion and feature level fusion | High spatial resolution, wide coverage, frequent revisit times, rich information content | Weather dependency, high cost, data processing complexity, limited temporal resolution | Road and pavement crack detection, bridge and infrastructure monitoring, urban building facade inspection, railway and highway crack monitoring |
Scale | Dataset/(Pixels × Pixels) | References |
---|---|---|
Image-based | 227 × 227 | [ , , , ] |
224 × 224 | [ ] | |
256 × 256 | [ ] | |
416 × 416 | [ ] | |
512 × 512 | [ ] | |
Patch-based | 128 × 128 | [ , ] |
200 × 200 | [ ] | |
224 × 224 | [ , , , , ] | |
227 × 227 | [ ] | |
256 × 256 | [ , ] | |
300 × 300 | [ , ] | |
320 × 480 | [ , ] | |
544 × 384 | [ ] | |
512 × 512 | [ , , , ] | |
584 × 384 | [ ] |
Model | Improvement/Innovation | Dataset | Backbone | Results |
---|---|---|---|---|
Faster R-CNN [ ] | Combined with drones for crack detection | 2000 images 5280 × 2970 pixels | VGG-16 | Precision = 92.03% Recall = 96.26% F1 score = 94.10% |
Faster R-CNN [ ] | Double-head structure is introduced, including an independent fully connected head and a convolution head | 1622 images 1612 × 1947 pixels | ResNet50 | AP = 47.2% |
Mask R-CNN [ ] | The morphological closing operation was incorporated into the M-R-101-FPN model to form an integrated model | 761 images 227 × 227 pixels | ResNets and VGG | Balanced accuracy = 81.94% F1 score = 68.68% IoU = 52.72% |
Mask R-CNN [ ] | PAFPN module and edge detection branch was introduced | 9680 images 1500 × 1500 pixels | ResNet-FPN | Precision = 92.03% Recall = 96.26% AP = 94.10% mAP = 90.57% Error rate = 0.57% |
Mask R-CNN [ ] | FPN structure introduces side join method and combines FPN with ResNet-101 to change RoI-Pooling layer to RoI-Align layer | 3430 images 1024 × 1024 pixels | ResNet101 | AP = 83.3% F1 score = 82.4% Average error = 2.33% mIoU = 70.1% |
YOLOv3-tiny [ ] | A structural crack detection and quantification method combined with structured light is proposed | 500 images 640 × 640 pixels | Darknet-53 | Accuracy = 94% Precision = 98% |
YOLOv4 [ ] | Some lightweight networks were used instead of the original backbone feature extraction network, and DenseNet, MobileNet, and GhostNet were selected for the lightweight networks | 800 images 416 × 416 pixels | DenseNet, MobileNet v1, MobileNet v2, MobileNet v3, and GhostNet | Precision = 93.96% Recall = 90.12% F1 score = 92% |
YOLOv4 [ ] | - | 1463 images 256 × 256 pixels | Darknet-53 | Accuracy = 92% mAP = 92% |
Datasets Name | Number of Images | Image Resolution | Manual Annotation | Scope of Applicability | Limitations |
---|---|---|---|---|---|
CrackTree200 [ ] | 206 images | 800 × 600 pixels | Pixel-level annotations for cracks | Crack classification and segmentation | With only 200 images, the dataset’s relatively small size can hinder the model’s ability to generalize across diverse conditions, potentially leading to overfitting on the specific examples provided |
Crack500 [ ] | 500 images | 2000 × 1500 pixels | Pixel-level annotations for cracks | Crack classification and segmentation | Limited number of images compared to larger datasets, which might affect the generalization of models trained on this dataset |
SDNET 2018 [ ] | 56000 images | 256 × 256 pixels | Pixel-level annotations for cracks | Crack classification and segmentation | The dataset’s focus on concrete surfaces may limit the model’s performance when applied to different types of surfaces or structures |
Mendeley data—crack detection [ ] | 40000 images | 227 × 227 pixels | Pixel-level annotations for cracks | Crack classification | The dataset might not cover all types of cracks or surface conditions, which can limit its applicability to a wide range of real-world scenarios |
DeepCrack [ ] | 2500 images | 512 × 512 pixels | Annotations for cracks | Crack segmentation | The resolution might limit the ability of models to capture very small or subtle crack features |
CFD [ ] | 118 images | 320 × 480 pixels | Pixel-level annotations for cracks | Crack segmentation | The dataset contains a limited number of data samples, which may limit the generalization ability of the model |
CrackTree260 [ ] | 260 images | 800 × 600 pixels and 960 × 720 pixels | Pixel-level labeling, bounding boxes, or other crack markers | Object detection and segmentation | Because the dataset is small, it can be easy for the model to overfit the training data, especially if you’re using a complex model |
CrackLS315 [ ] | 315 images | 512 × 512 pixels | Pixel-level segmentation mask or bounding box | Object detection and segmentation | The small size of the dataset may make the model perform poorly in complex scenarios, especially when encountering different types of cracks or uncommon crack features |
Stone331 [ ] | 331 images | 512 × 512 pixels | Pixel-level segmentation mask or bounding box | Object detection and segmentation | The relatively small number of images limits the generalization ability of the model, especially in deep learning tasks where smaller datasets tend to lead to overfitting |
Index | Index Value and Calculation Formula | Curve |
---|---|---|
True positive | - | |
False positive | - | |
True negative | - | |
False negative | - | |
Precision | PRC | |
Recall | PRC, ROC curve | |
F1 score | F1 score curve | |
Accuracy | Accuracy vs. threshold curve | |
Average precision | PRC | |
Mean average precision | - | |
IoU | IoU distribution curve, precision-recall curve with IoU thresholds |
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Yuan, Q.; Shi, Y.; Li, M. A Review of Computer Vision-Based Crack Detection Methods in Civil Infrastructure: Progress and Challenges. Remote Sens. 2024 , 16 , 2910. https://doi.org/10.3390/rs16162910
Yuan Q, Shi Y, Li M. A Review of Computer Vision-Based Crack Detection Methods in Civil Infrastructure: Progress and Challenges. Remote Sensing . 2024; 16(16):2910. https://doi.org/10.3390/rs16162910
Yuan, Qi, Yufeng Shi, and Mingyue Li. 2024. "A Review of Computer Vision-Based Crack Detection Methods in Civil Infrastructure: Progress and Challenges" Remote Sensing 16, no. 16: 2910. https://doi.org/10.3390/rs16162910
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Janeway CA Jr, Travers P, Walport M, et al. Immunobiology: The Immune System in Health and Disease. 5th edition. New York: Garland Science; 2001.
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Infectious agents and how they cause disease.
Infectious disease can be devastating, and sometimes fatal, to the host. In this part of the chapter we will briefly examine the stages of infection, and the various types of infectious agents.
The process of infection can be broken down into stages, each of which can be blocked by different defense mechanisms. In the first stage, a new host is exposed to infectious particles shed by an infected individual. The number, route, mode of transmission, and stability of an infectious agent outside the host determines its infectivity. Some pathogens, such as anthrax, are spread by spores that are highly resistant to heat and drying, while others, such as the human immunodeficiency virus ( HIV ), are spread only by the exchange of bodily fluids or tissues because they are unable to survive as infectious agents outside the body.
The first contact with a new host occurs through an epithelial surface. This may be the skin or the internal mucosal surfaces of the respiratory, gastro-intestinal, and urogenital tracts. After making contact, an infectious agent must establish a focus of infection. This involves adhering to the epithelial surface, and then colonizing it, or penetrating it to replicate in the tissues ( Fig. 10.2 , left-hand panels). Many microorganisms are repelled at this stage by innate immunity . We have discussed the innate immune defense mediated by epithelia and by phagocytes and complement in the underlying tissues in Chapter 2. Chapter 2 also discusses how NK cells are activated in response to intracellular infections, and how a local inflammatory response and induced cytokines and chemokines can bring more effector cells and molecules to the site of an infection while preventing pathogen spread into the blood. These innate immune responses use a variety of germline-encoded receptors to discriminate between microbial and host cell surfaces, or infected and normal cells. They are not as effective as adaptive immune responses, which can afford to be more powerful on account of their antigen specificity . However, they can prevent an infection being established, or failing that, contain it while an adaptive immune response develops.
Infections and the responses to them can be divided into a series of stages. These are illustrated here for an infectious microorganism entering across an epithelium, the commonest route of entry. The infectious organism must first adhere to epithelial (more...)
Only when a microorganism has successfully established a site of infection in the host does disease occur, and little damage will be caused unless the agent is able to spread from the original site of infection or can secrete toxins that can spread to other parts of the body. Extracellular pathogens spread by direct extension of the focus of infection through the lymphatics or the bloodstream. Usually, spread by the bloodstream occurs only after the lymphatic system has been overwhelmed by the burden of infectious agent. Obligate intracellular pathogens must spread from cell to cell; they do so either by direct transmission from one cell to the next or by release into the extracellular fluid and reinfection of both adjacent and distant cells. Many common food poisoning organisms cause pathology without spreading into the tissues. They establish a site of infection on the epithelial surface in the lumen of the gut and cause no direct pathology themselves, but they secrete toxins that cause damage either in situ or after crossing the epithelial barrier and entering the circulation.
Most infectious agents show a significant degree of host specificity , causing disease only in one or a few related species. What determines host specificity for every agent is not known, but the requirement for attachment to a particular cell-surface molecule is one critical factor. As other interactions with host cells are also commonly needed to support replication, most pathogens have a limited host range. The molecular mechanisms of host specificity comprise an area of research known as molecular pathogenesis, which falls outside the scope of this book.
While most microorganisms are repelled by innate host defenses, an initial infection, once established, generally leads to perceptible disease followed by an effective host adaptive immune response . This is initiated in the local lymphoid tissue, in response to antigens presented by dendritic cells activated during the course of the innate immune response ( Fig. 10.2 , third and fourth panels). Antigen-specific effector T cells and antibody -secreting B cells are generated by clonal expansion and differentiation over the course of several days, during which time the induced responses of innate immunity continue to function. Eventually, antigen -specific T cells and then antibodies are released into the blood and recruited to the site of infection ( Fig. 10.2 , last panel). A cure involves the clearance of extracellular infectious particles by antibodies and the clearance of intracellular residues of infection through the actions of effector T cells.
After many types of infection there is little or no residual pathology following an effective primary response. In some cases, however, the infection or the response to it causes significant tissue damage. In other cases, such as infection with cytomegalovirus or Mycobacterium tuberculosis , the infection is contained but not eliminated and can persist in a latent form. If the adaptive immune response is later weakened, as it is in acquired immune deficiency syndrome ( AIDS ), these diseases reappear as virulent systemic infections. We will focus on the strategies used by certain pathogens to evade or subvert adaptive immunity and thereby establish a persistent infection in the first part of Chapter 11.
In addition to clearing the infectious agent, an effective adaptive immune response prevents reinfection. For some infectious agents, this protection is essentially absolute, while for others infection is reduced or attenuated upon reexposure.
The agents that cause disease fall into five groups: viruses, bacteria , fungi, protozoa, and helminths (worms). Protozoa and worms are usually grouped together as parasites, and are the subject of the discipline of parasitology, whereas viruses, bacteria, and fungi are the subject of microbiology. In Fig. 10.3 , the classes of microorganisms and parasites that cause disease are listed, with typical examples of each. The remarkable variety of these pathogens has caused the natural selection of two crucial features of adaptive immunity. First, the advantage of being able to recognize a wide range of different pathogens has driven the development of receptors on B and T cells of equal or greater diversity. Second, the distinct habitats and life cycles of pathogens have to be countered by a range of distinct effector mechanisms. The characteristic features of each pathogen are its mode of transmission, its mechanism of replication, its pathogenesis or the means by which it causes disease, and the response it elicits. We will focus here on the immune responses to these pathogens.
A variety of microorganisms can cause disease. Pathogenic organisms are of five main types: viruses, bacteria, fungi, protozoa, and worms. Some common pathogens in each group are listed in the column on the right.
Infectious agents can grow in various body compartments, as shown schematically in Fig. 10.4 . We have already seen that two major compartments can be defined—intracellular and extracellular. Intracellular pathogens must invade host cells in order to replicate, and so must either be prevented from entering cells or be detected and eliminated once they have done so. Such pathogens can be subdivided further into those that replicate freely in the cell, such as viruses and certain bacteria (species of Chlamydia and Rickettsia as well as Listeria ), and those, such as the mycobacteria, that replicate in cellular vesicles. Viruses can be prevented from entering cells by neutralizing antibodies whose production relies on T H 2 cells (see Section 9-14 ), while once within cells they are dealt with by virus-specific cytotoxic T cells , which recognize and kill the infected cell (see Section 8-21 ). Intravesicular pathogens, on the other hand, mainly infect macrophages and can be eliminated with the aid of pathogen-specific T H 1 cells , which activate infected macrophages to destroy the pathogen (see Section 8-26 ).
Pathogens can be found in various compartments of the body, where they must be combated by different host defense mechanisms. Virtually all pathogens have an extracellular phase where they are vulnerable to antibody-mediated effector mechanisms. However, intracellular (more...)
Many microorganisms replicate in extracellular spaces, either within the body or on the surface of epithelia. Extracellular bacteria are usually susceptible to killing by phagocytes and thus pathogenic species have developed means of resisting engulfment. The encapsulated gram-positive cocci, for instance, grow in extracellular spaces and resist phagocytosis by means of their polysaccharide capsule. This means they are not immediately eliminated by tissue phagocytes on infecting a previously unexposed host. However, if this mechanism of resistance is overcome by opsonization by complement and specific antibody , they are readily killed after ingestion by phagocytes. Thus, these extracellular bacteria are cleared by means of the humoral immune response (see Chapter 9).
Different infectious agents cause markedly different diseases, reflecting the diverse processes by which they damage tissues ( Fig. 10.5 ). Many extracellular pathogens cause disease by releasing specific toxic products or protein toxins (see Fig. 9.23 ), which can induce the production of neutralizing antibodies (see Section 9-14 ). Intracellular infectious agents frequently cause disease by damaging the cells that house them. The specific killing of virus-infected cells by cytotoxic T cells thus not only prevents virus spread but removes damaged cells. The immune response to the infectious agent can itself be a major cause of pathology in several diseases (see Fig. 10.5 ). The pathology caused by a particular infectious agent also depends on the site in which it grows; Streptococcus pneumoniae in the lung causes pneumonia, whereas in the blood it causes a rapidly fatal systemic illness.
Pathogens can damage tissues in a variety of different ways. The mechanisms of damage, representative infectious agents, and the common names of the diseases associated with each are shown. Exotoxins are released by microorganisms and act at the surface (more...)
As we learned in Chapter 2, for a pathogen to invade the body, it must first bind to or cross the surface of an epithelium. When the infection is due to intestinal pathogens such as Salmonella typhi , the causal agent of typhoid fever, or Vibrio cholerae , which causes cholera, the adaptive immune response occurs in the specialized mucosal immune system associated with the gastrointestinal tract, as described later in this chapter. Some intestinal pathogens even target the M cells of the gut mucosal immune system, which are specialized to transport antigens across the epithelium, as a means of entry.
Many pathogens cannot be entirely eliminated by the immune response . But neither are most pathogens universally lethal. Those pathogens that have persisted for many thousands of years in the human population are highly evolved to exploit their human hosts, and cannot alter their pathogenicity without upsetting the compromise they have achieved with the human immune system . Rapidly killing every host it infects is no better for the long-term survival of a pathogen than being wiped out by the immune response before it has had time to infect another individual. In short, we have learned to live with our enemies, and they with us. However, we must be on the alert at all times for new pathogens and new threats to health. The human immunodeficiency virus that causes AIDS serves as a warning to mankind that we remain constantly vulnerable to the emergence of new infectious agents.
The mammalian body is susceptible to infection by many pathogens, which must first make contact with the host and then establish a focus of infection in order to cause infectious disease. To establish an infection, the pathogen must first colonize the skin or the internal mucosal surfaces of the respiratory, gastrointestinal, or urogenital tracts and then overcome or bypass the innate immune defenses associated with the epithelia and underlying tissues. If it succeeds in doing this, it will provoke an adaptive immune response that will take effect after several days and will usually clear the infection. Pathogens differ greatly in their lifestyles and means of pathogenesis, requiring an equally diverse set of defensive responses from the host immune system .
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IMAGES
COMMENTS
Introduction. An infectious disease can be defined as an illness due to a pathogen or its toxic product, which arises through transmission from an infected person, an infected animal, or a contaminated inanimate object to a susceptible host. Infectious diseases are responsible for an immense global burden of disease that impacts public health systems and economies worldwide, disproportionately ...
infectious disease, in medicine, a process caused by an agent, often a type of microorganism, that impairs a person's health.In many cases, infectious disease can be spread from person to person, either directly (e.g., via skin contact) or indirectly (e.g., via contaminated food or water). An infectious disease can differ from simple infection, which is the invasion of and replication in the ...
Global change, including climate change, urbanization and global travel and trade, has affected the emergence and spread of infectious diseases. In the Review, Baker, Metcalf and colleagues ...
Infectious diseases can be caused by several different classes of pathogenic organisms (commonly called germs). These are viruses, bacteria, protozoa, and fungi.Almost all of these organisms are microscopic in size and are often referred to as microbes or microorganisms.. Although microbes can be agents of infection, most microbes do not cause disease in humans.
IVPrevention and Treatment. Infectious disease may be an unavoidable fact of life, but there are many strategies available to help us protect ourselves from infection and to treat a disease once it has developed. Some are simple steps that individuals can take; others are national or global methods of detection, prevention, and treatment.
Usually, there are four main types of diseases: infectious diseases, deficiency diseases, hereditary diseases, and physiological diseases. In terms of the transmission chance of any disease, it can either be communicable or noncommunicable. ... While a disease-causing agent is different from current strains and much more infectious, the ...
Today, we know that John Snow was dealing with cholera, an infectious disease caused by the bacterium Vibrio cholerae. People can develop cholera from contaminated food and water, which leads to symptoms including vomiting and diarrhea. In 19 th century London, outbreaks were relatively common because human waste was often thrown into rivers ...
Better Essays. 2861 Words; 12 Pages; Open Document. INTRODUCTION This report will be analysing the different types of infectious diseases and their characteristics defining the meaning of pathogens, types of immunities and differentiating the term immunity and immunisation. It also 1.1
An easy way to catch most infectious diseases is by coming in contact with a person or an animal with the infection. Infectious diseases can be spread through direct contact such as: Person to person. Infectious diseases commonly spread through the direct transfer of bacteria, viruses or other germs from one person to another.
Infectious Diseases. Infectious diseases have a wide variety of causes, and NIAID supports research to control and prevent diseases caused by virtually all human infectious agents. NIAID provides funding opportunities and a comprehensive set of resources for researchers that support basic research, pre-clinical development, and clinical evaluation.
Describe the different types of disease reservoirs; Compare contact, vector, and vehicle modes of transmission; Identify important disease vectors; Explain the prevalence of nosocomial infections; Understanding how infectious pathogens spread is critical to preventing infectious disease.
The world has developed an elaborate global health system as a bulwark against known and unknown infectious disease threats. The system consists of various formal and informal networks of organizations that serve different stakeholders; have varying goals, modalities, resources, and accountability; operate at different regional levels (i.e., local, national, regional, or global); and cut ...
Diseases can be of two types. Infectious diseases; Non-infectious diseases; Infectious Diseases. Diseases that spread from one person to another are called communicable diseases. They are usually caused by microorganisms called pathogens (fungi, rickettsia, bacteria, viruses, protozoans, and worms).
Communicable diseases are illnesses caused by viruses or bacteria that people spread to one another through contact with contaminated surfaces, bodily fluids, blood products, insect bites, or through the air.[1] There are many examples of communicable diseases, some of which require reporting to appropriate health departments or government agencies in the locality of the outbreak.
What these studies found was this: susceptibility to practically every, major, non-infectious disease rarely lies in the genetic "notes" themselves. Rather, about 90% of it lies in the ...
Cracks are a common defect in civil infrastructures, and their occurrence is often closely related to structural loading conditions, material properties, design and construction, and other factors. Therefore, detecting and analyzing cracks in civil infrastructures can effectively determine the extent of damage, which is crucial for safe operation. In this paper, Web of Science (WOS) and Google ...
Figure 10.3. A variety of microorganisms can cause disease. Pathogenic organisms are of five main types: viruses, bacteria, fungi, protozoa, and worms. Some common pathogens in each group are listed in the column on the right. Infectious agents can grow in various body compartments, as shown schematically in Fig. 10.4.