Category: 8. Health

  • Mammography has role in diagnosing pregnancy-associated breast cancer

    Mammography has role in diagnosing pregnancy-associated breast cancer

    Mammography may have a diagnostic role in evaluating pregnancy-associated breast cancer, researchers have found.

    Mammography found about four out of five of these breast cancers and shows calcifications in more than half of pregnancy-associated breast cancer cases, wrote a team led by Noam Nissan, MD, PhD, from the Memorial Sloan Kettering Cancer Center in New York. The results were published July 31 in Clinical Imaging. 

    “Despite the high proportions of increased mammographic density, mammography successfully demonstrated most pregnancy-associated breast cancers and frequently provided valuable additional information for their evaluation,” the Nissan team wrote. 

    Pregnancy-associated breast cancer is uncommon, occurring in about one in 3,000 pregnancies. However, previous reports suggest that incidence rate is rising in developed countries due to the trend of delayed childbirth. Radiological diagnosis can be challenging due to physiologic changes in breast tissue that pregnant women experience, which can mask abnormalities.  

    Ultrasound is the go-to modality for these cases, but the researchers noted that mammography’s role in this area remains unclear due to the increased fibroglandular tissue during pregnancy and lactation. 

    Nissan and colleagues implemented a mammography strategy at their institution, evaluating its role in the diagnostic workup of pregnancy-associated breast cancer. 

    Final analysis included data from 167 women with newly diagnosed pregnancy-associated breast cancer and an average age of 37 years. The women were diagnosed between 2009 and 2024. Of the total, 30 women were pregnant and 137 were lactating at the time they were diagnosed. In the study, 163 women (97.6%) had dense breasts and 135 (80.8%) had extremely dense breasts. 

    Mammography showed 137 (82%) of the total pregnancy-associated breast cancers. This also included 21 cases where mammography was the only detection method, 17 cases that had additional positive stereotactic biopsy, 35 cases that showed changes in lesion size by ≥1 cm (p < 0.001), and 35 cases where T-staging was changed. 

    And compared with ultrasound alone, excluding cases with duplicate contributions, mammography added value in 64 patients (38.3%). 

    The study authors highlighted that, however these cancers present, mammography and ultrasound “must serve as complementary tools in the diagnostic evaluation of pregnancy-associated breast cancers.” They also stressed that there are safety concerns regarding the use of mammography in this population. 

    “Mammography is considered safe when clinically indicated, as the fetal radiation dose from a standard four-view mammogram is extremely low, typically counting for less than 0.03 mGy, and well below the threshold associated with deterministic effects such as teratogenesis, which require exposures of at least 50 mGy,” the authors wrote. 

    The full study can be accessed here.

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  • You May Not Need a Blood Test With New Biomedical Technology

    You May Not Need a Blood Test With New Biomedical Technology

    People with diabetes need to check their blood glucose multiple times a day. It’s not uncommon for patients with diabetes to monitor glucose levels at least five times a day. Discrete, on-demand testing requires pricking your finger for a blood sample or inserting a microfilament sensor in the body for continuous glucose monitoring.

    The daily routine of glucose testing is often a major inconvenience for most people with diabetes, and there are always risks, said Makarand Paranjape, an associate professor of physics and director of the Georgetown Nanoscience and Microfabrication Cleanroom Lab (GNuLab) in the College of Arts & Sciences.

    Makarand Paranjape is an associate professor of physics and director of the Georgetown Nanoscience and Microfabrication Cleanroom Lab (GNuLab) in the College of Arts & Sciences.

    “You’re inserting a needle into your arm or abdomen and putting a sensor inside the body to detect blood glucose. Anytime you put something in your body, it’s going to be attacked by your own immune system,” Paranjape said. 

    Paranjape hopes to decrease those risks with his new non-invasive technology, a transdermal patch that can detect biomarkers typically found in the bloodstream without drawing blood or inserting any device into the body.

    What started as a project initially funded by the Department of Defense 25 years ago has since become one of Paranjape’s passionate pursuits. Over the last two decades, the physics professor has refined his biomedical technology and developed a family of patents through Georgetown’s Office of Technology Commercialization that he hopes will positively impact the quality of life for patients suffering from a wide variety of chronic disease conditions.

    A Better Way to Detect Biomarkers

    Blood tests help detect biomarkers that physicians use to treat patients, from diabetes to heart disease and even some forms of cancer.

    While these biomarkers exist in the bloodstream, they also permeate through the capillaries and into the liquid surrounding cells in body tissue known as interstitial fluid. Detecting biomarkers in this fluid is even easier than in the blood because the larger components of blood, like platelets, white and red blood cells, can’t pass through the natural filtration mechanism provided by capillaries.

    Paranjape with two students in the lab
    Paranjape with two of his graduate students, Karma Dema (G’29) (left) and Darrian Mills (G’26) (right).

    “The interstitial fluid, sometimes also called extracellular fluid, bathes every living cell in your body,” Paranjape said. “It’s like a pre-filtered sample. When you draw blood, you have to filter down all the other stuff you don’t need. We don’t have to do that, so the interstitial fluid is ideal for detecting blood-borne biomarkers or biomolecules.”

    Paranjape engineered a patch that non-invasively samples interstitial fluid.

    Paranjape says his invention is akin to a Band-Aid. Each patch contains an array of microheaters, each of which is about the diameter of a single strand of human hair. For just a few milliseconds, these microheaters reach 100 degrees Celsius to create a temporary micropore in only the top-most layer of skin to access and collect interstitial fluid.

    “That highly-controlled thermal pulse effectively removes only a microscopic portion of the top-most layer of dead skin. It’s essentially exfoliating that small area of skin to an extent that you’re creating a hair-sized micropore from the top of the skin extending to the living tissue,” he said. “Once you get through that layer, there is plenty of  interstitial fluid that actually comes up and out of the micropore since your heartbeat is providing pressure.”

    Two blue gloved hands handle a circular disc
    Paranjape’s patch technology (pictured on a silicon handle wafer) consists of flexible polymers on an adhesive using gold metal microheaters to create micropores in the skin that allow for the collection of interstitial fluid.

    Whereas existing solutions that sample interstitial fluid require a sensor inserted into the body, Paranjape’s device allows the interstitial fluid to exude naturally from the pores after the microheaters activate, making it noninvasive. His device is also pain-free, verified in a pilot clinical trial, since the temperatures applied and micropores generated are both shallow and don’t reach the skin’s nerve endings.

    It will also require less maintenance from patients. Whereas some patients with diabetes may not monitor their blood glucose enough during the day because they forget or are apprehensive of the pain from a pinprick, the patch can potentially monitor biomarkers on its own, Paranjape said. Patients would only have to change the patch once a day.

    While Paranjape has primarily developed the patch in the context of diabetes, he’s testing his patch to draw biomarkers for other disease conditions, such as traumatic brain injury.

    Transforming Drug Delivery

    Paranjape is now working on the next step of his biomedical patch: drug delivery.

    Transdermal patches that deliver drugs already exist, such as nicotine patches that people use to wean themselves off of smoking. However, current patches are less efficient because they require existing drugs to be modified, Paranjape said.

    Mak Paranjape in a lab coat with two students looking at a lab device“Most of these patches require the drug in question to be tailored chemically to allow it to penetrate through intact skin. Ours does not,” he said. “We can use off-the-shelf drugs. We are creating tiny pores through the skin so the drug can easily enter and diffuse to the circulatory system.”

    Paranjape’s lab is experimenting with loading drugs into his patch that can be administered after the microheaters activate. The patch could also allow patients to schedule release times and determine the correct dosing.

    Paranjape also said his patch is promising because transdermal drug delivery could reduce the dosages patients need and reduce medical waste.

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  • Altered inter-hemispheric and intra-hemispheric functional connectivity dynamics in male cigarette smokers | BMC Psychiatry

    Altered inter-hemispheric and intra-hemispheric functional connectivity dynamics in male cigarette smokers | BMC Psychiatry

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  • Why Vitamin D and B12 deficiencies are so common in Indians and what to do about them

    Why Vitamin D and B12 deficiencies are so common in Indians and what to do about them

    A recent social media post highlighting the widespread issue of Vitamin D and Vitamin B12 deficiencies in Indian populations has gone viral. The post on X has sparked important conversations about these often-overlooked health issues.

    The user urged for a ‘polio-like drive’ to raise awareness and address these deficiencies.

    “India desperately needs another polio-like drive but for adults with Vitamin D + B12 deficiency,” the post read. Several studies suggest that a large portion of the Indian population, across age groups and lifestyles, is affected by these deficiencies.
    Symptoms

    Symptoms of Vitamin D insufficiency include weakness, lethargy, tiredness, bone issues such as osteoporosis, which can cause fractures, and rickets in children.

    Similarly, Vitamin B12 is vital for the growth and function of our nervous system, as well as the generation of blood cells.

    Its deficiency can cause megaloblastic anaemia and a variety of neurological system problems, including imbalance, tingling or numbness in the extremities, and memory problems.

    Why are Vitamin D and B12 deficiencies so common in Indians

    According to the National Library of Medicine, Vitamin D insufficiency is widespread across India, with prevalence rates ranging from 70% to 100% in the general population. In India, commonly consumed foods like dairy products are rarely fortified with vitamin D. Indian socioreligious and cultural practices do not promote proper sun exposure, undermining the potential benefits of abundant sunshine.

    As a result, subclinical vitamin D deficiency is common in both urban and rural areas, as well as among all socioeconomic and geographic strata. A high prevalence of vitamin D deficiency in India is also linked to the infrequent fortification of commonly consumed foods with vitamin D.

    According to Subhrojyoti Bhowmick, medical superintendent, Peerless Hospital, Kolkata, “Vitamin D deficiency is rapidly gaining epidemic proportions, yet it is the most under-diagnosed and under-treated nutritional deficiency in the world.”

    “Vitamin D, which can be synthesised in the body on sun exposure, is essential to maintain calcium homeostasis in the body for good bone health,” reported The Times of India (2015).

    Vitamin B12 insufficiency is thought to be common in the Indian population. Vitamin B12 insufficiency is prevalent in the north Indian population, accounting for 47%, according to the National Library of Medicine.

    People with diabetes have higher vitamin B12 levels than the general population, although the prevalence of insufficiency remains significant.

    “Vitamin D and B12 deficiencies are alarmingly prevalent in India, impacting a large proportion of adults,” Dr Rakesh Gupta, Senior Consultant, Internal Medicine, Indraprastha Apollo Hospitals, was quoted as saying by The Financial Express.

    “Urbanised, indoor-centric lifestyles, cultural clothing that covers most of the skin, and sunscreen use all limit natural Vitamin D synthesis,” he added.

    According to Dr Gupta, B12 is mostly found in animal-based diets, making India’s vast vegetarian population particularly vulnerable. “Malabsorption due to conditions like celiac disease or gastrointestinal infections also contributes to the problem.”

    How to prevent and manage vitamin deficiencies:

    To address vitamin deficiencies, focus on dietary changes, supplementation, and addressing underlying medical conditions. Consulting a healthcare professional is crucial for proper diagnosis and treatment.

    Here are a few of the most common treatment approaches:

    Eat things recommended by your healthcare provider to get more of the vitamins you need.

    Fortified foods contain additional nutrients. Enriched foods contain additional nutrients to compensate for those lost during processing. Milk containing vitamins A, B2, or D is an example of a fortified food. Enriched foods include flour, sugar and certain food oils with added A or B vitamins.

    Sunlight is a natural and efficient way for your body to produce vitamin D, often called the ‘sunshine vitamin’.

    Oral supplements are widely available over the counter at pharmacies and grocery stores. For individuals experiencing vitamin deficiencies, higher doses may be prescribed by a healthcare provider.

    When vitamin levels are severely low, a healthcare provider can prescribe vitamin injections or intravenous (IV) infusions to quickly replenish the body’s supply.

    Additionally, some vitamins are available in patch form, allowing for slow absorption through the skin.

    Are vitamin deficiencies preventable?

    Vitamin deficiencies are normally avoided, although they do occur for causes beyond your control. That is especially true with your genetic diseases or other circumstances. You can still develop them even if you eat healthy foods. However, eating a wide range of food rich in vitamins and other critical nutrients is the most effective method to avoid or lower your chances of acquiring these conditions.

    Vegetables, fruits, and lean protein are some of these examples. Another important measure you may take is to visit your primary care provider at least once a year.

    Regular checkups typically include blood testing, which can detect vitamin deficiencies before you experience or notice a symptom.

    “Early detection and awareness can prevent immense suffering. Just as India eradicated polio through a mass movement, we need a similar drive against Vitamin D and B12 deficiencies,” Dr Gupta added.

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  • Multiple sclerosis could affect health years before classic symptoms

    Multiple sclerosis could affect health years before classic symptoms

    New research from Canada’s University of British Columbia found patients diagnosed with multiple sclerosis started to experience new health issues up to 15 years before showing the classic symptoms of the condition. Photo by Adobe Stock/HealthDay

    Aug. 4 (UPI) — People with multiple sclerosis, or MS, begin experiencing new health issues up to 15 years before the classic signs of the illness appear, Canadian research shows.

    “MS can be difficult to recognize as many of the earliest signs — like fatigue, headache, pain and mental health concerns — can be quite general and easily mistaken for other conditions,” study senior author Helen Tremlett noted.

    “Our findings dramatically shift the timeline for when these early warning signs are thought to begin, potentially opening the door to opportunities for earlier detection and intervention,” said Tremlett, a professor of neurology at the University of British Columbia in Vancouver.

    Her team published its findings Friday in JAMA Network Open.

    MS is an autoimmune illness in which the body’s immune system goes awry, attacking the protective myelin sheath that surrounds nerves within the brain and along the spinal cord.

    Communication breaks down between the brain and the body, leading to progressive disability.

    The new study that suggests MS may begin more than a decade before a clinical diagnosis.

    Tremlett’s group combed through British Columbia health data for 12,000 people with or without MS. Records went back as far as 25 years prior to an MS patient’s diagnosis.

    That’s far longer than the five to 10 years covered in earlier studies looking at disease onset.

    The main findings:

    Fifteen years before the onset of classic MS symptoms, there was a noticeable uptick in patient visits to their general practitioner, or in visits to specialists for issues like fatigue, pain and dizziness, as well as mental health issues such as anxiety and depression.

    Twelve years before symptom onset, visits to psychiatrists began to rise.

    Eight to nine years before, visits to neurologists and eye specialists rose, perhaps linked to issues such as blurred vision or eye pain.

    Three to five years before, researchers noted a rise in visits to emergency departments and/or radiology facilities.

    One year before, visits peaked for a wide range of different physician types, such as neurologists, radiologists and emergency doctors.

    “These patterns suggest that MS has a long and complex prodromal phase — where something is happening beneath the surface but hasn’t yet declared itself as MS,” said study first author Dr. Marta Ruiz-Algueró, a postdoctoral fellow at UBC.

    “We’re only now starting to understand what these early warning signs are, with mental health-related issues appearing to be among the earliest indicators,” she added in a news release.

    The researchers stressed that the early issues that drove MS patients to seek out care can be caused by a myriad of health conditions. Simply experiencing these conditions does not mean a person will go on to develop MS.

    Nevertheless, “By identifying these earlier red flags, we may eventually be able to intervene sooner — whether that’s through monitoring, support or preventive strategies,” Tremlett said a university news release. “It opens new avenues for research into early biomarkers, lifestyle factors and other potential triggers that may be at play during this previously overlooked phase of the disease.”

    More information

    Find out more about multiple sclerosis at the National Multiple Sclerosis Society

    Copyright © 2025 HealthDay. All rights reserved.

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  • STEC Illnesses in England Rose by 26 Percent in 2024, Non-O157 STEC Cases Tripled Since 2019

    STEC Illnesses in England Rose by 26 Percent in 2024, Non-O157 STEC Cases Tripled Since 2019

    England saw a 26 percent rise in Shiga toxin-producing Escherichia coli (STEC) infections from 2023 to 2024, according to the latest data from the UK Health Security Agency (UKHSA). The rise in cases may be attributable to one foodborne illness outbreak involving contaminated salad leaves, officials say.

    Of the 2,544 laboratory confirmed STEC cases seen in England in 2024, 564 were STEC O157 and 1,980 were non-O157 serotypes. Hemolytic uremic syndrome (HUS) developed in 2.1 percent of STEC O157 patients and 1.7 percent of non-O157 patients. Among STEC O157 cases, two people died, and among non-O157 cases, five people died. The highest incidence of STEC cases in 2024 was in children between one and four years of age.

    In 2024, UKHSA and partner agencies investigated five STEC outbreaks—all of which were non-O157—comprising 467 cases (348 in England specifically). The sources for three of the outbreaks were contaminated beef, fresh fruit, and salad leaves. The largest outbreak was linked to contaminated leafy greens which resulted in 293 cases (196 cases in England). Of the 293 cases, 126 people were hospitalized, 11 developed HUS, and two died.

    STEC non-O157 cases in 2024 increased nearly three times since 2019, while O157 cases have returned to pre-COVID-19 pandemic levels. The increase in STEC non-O157 seen in 2024 is due to the outbreak linked to salad leaves. Additionally, more cases of illness are being detected due to the growing use of polymerase chain reaction (PCR) testing technology in laboratories.

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  • Perioperative outcomes among older surgical patients with multimorbidity: a longitudinal study from Ethiopia | BMC Public Health

    Perioperative outcomes among older surgical patients with multimorbidity: a longitudinal study from Ethiopia | BMC Public Health

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  • Climate change, armed conflict, forced displacement, and epidemic-prone diseases: an exploratory study in northern Syria | BMC Public Health

    Climate change, armed conflict, forced displacement, and epidemic-prone diseases: an exploratory study in northern Syria | BMC Public Health

    Conflict, environmental factors, and disease incidence are increasingly intertwined. Our study finds that several of the environmental factors evaluated were associated with changes in suspected diarrheal disease and respiratory infections in northern Syria, but in line with known biology, these associations were not often consistent between the two disease types. We also note that the combination of high levels of displacement and conflict was consistently associated with increased risk of both suspected diarrheal disease and respiratory infections, which accords with existing literature [42]. Syria has already experienced severe repercussions of climate change on its environment (e.g., through droughts, floods, and extreme heat), and this will likely continue in the future. We note that northeast Syria is particularly affected with higher temperatures in both the dry and wet seasons, less surface vegetation, and less surface water than northwest Syria; it is also more affected by interruptions of its key water stations, particularly Allouk water station. This may partially explain the higher proportionate morbidity, especially for suspected diarrheal disease, in northeastern governorates. Understanding the relationships between these environmental factors and infectious diseases, while also incorporating relevant conflict-related factors, is critical to prepare for changing disease burdens resulting from shifting environments. It can also help support regional and seasonal mitigation strategies given the localized differences seen in environmental factors and the burden of infectious diseases.

    Mean temperature, precipitation, surface water, and vegetation levels are particularly relevant in the Syrian context. We found that increasing mean temperatures were associated with decreased risk of suspected respiratory infections but increased risk of suspected diarrheal disease across a lag period extending to 8 and 4 weeks respectively, which accords with prior work. There is a plausible biological-environmental mechanism for these relationships wherein, for instance, lower temperatures cause lower humidity and conditions facilitating respiratory transmission [43]. Warming temperatures in Syria have already largely outpaced global mean temperature increases (with no indication of cessation), indicating that risk of suspected diarrheal diseases may likewise increase [14, 15]. However, decreasing precipitation levels, which Syria has experienced since 1990, may increase suspected respiratory infection risk while decreasing risk of suspected diarrheal disease [10, 11]. We also found that vegetation levels were associated with reduced suspected respiratory infection risk in the same week but increased risk after 8 weeks. There are several possible explanations for this. Higher vegetation levels may create, over several weeks, environments that are more hospitable to disease spread. This could be through multiple pathways, including interactions with other environmental factors, increasing exposure to irritants (e.g., mold and pollen) that are risk factors for respiratory infections, or changing individual-level patterns, gradually increasing exposures and impacting health behaviors. The findings may also reflect different seasonality between NDVI and suspected respiratory infections, whereby their respective seasonal peaks occur at different times. While we account for seasonality with a week-of-year spline, there may be additional seasonality components that are meaningful to this relationship.

    In Syria, as in elsewhere, it is important to note that changes in environmental factors are neither unidimensional nor consistent across time and space. While Syria is generally receiving less rainfall than before, there have been several instances of heavy rainfall (which likely also impacts vegetation levels) with widespread flooding, particularly affecting northwest Syria [44]. Such flooding may acutely increase the risk of suspected respiratory infections in the subsequent weeks, as pooled waters may harbor pathogens and other contaminants deleterious for respiratory health [45]. Flooding may also increase displacement and housing overcrowding in many contexts, which is an ideal context for respiratory transmission. This illustrates that when considering the potential effects of environmental events on disease incidence, long-term mean changes and as well as short-term climatic events must both be considered, as must unique spatial and temporal contexts. Likewise, it also highlights that certain diseases can be expected to increase by mode of transmission, according to changes in climate variables.

    We also found the interaction between conflict and climate to be significant for both disease types. Conflict has been linked to increases in infectious disease risk throughout the literature, including in Yemen and Sudan [46, 47]. Though we tested each model with conflict and displacement modeled independently, we found that the interaction term produced a stronger model fit and is more consistent with the reality in northern Syria of intense conflict followed by mass displacement during the study period. Increased risk of suspected diarrheal disease at lower levels of conflict and displacement may be triggered by single but large-impact conflict events relating to destruction of water-related infrastructure, which could have a rapid effect on both displacement and waterborne disease cases. Allouk water station in Al-Hasakah is one example of this. The water station serves over 460,000 individuals in northeast Syria yet has faced recurrent direct attacks, discriminatory operation by the Turkish government (e.g., the station was cut off for five days in 2020 without reason), and its use in bargaining. This politicalization impeded the response to the 2022–2023 cholera outbreak in the region as regions downstream of Allouk were unable to access adequate water [48,49,50].

    Two strengths of the study include biologically plausible findings and high explanatory power of the variables used. However, this study does have its limitations. The surveillance data used captures suspected but not laboratory-confirmed cases of disease amongst those persons who have access to health care, and is therefore biased against the specificity of case definitions in favor of the sensitivity of disease detection. In this exploratory analysis, we considered environmental variables and their lags individually in our model and did not consider any potential interplay between them. Future work should consider a conceptual causal model in evaluating linkages. The environmental variables may represent similar impacts on disease incidence due to their linkages in the causal chain. For instance, increased humidity may be a consequence of increased precipitation and exhibit a single effect. Each model was initially run with all environmental variables and their lags specified non-parametrically, though few exhibited non-linear behavior and thus we opted to specify them linearly to increase the models’ interpretability.

    This study also uses a coarse measure of displacement, based on the available governorate-level data, that required several assumptions to move from a monthly governorate-level measurement to a weekly district-level. We evenly distributed the total reported monthly displacement for each governorate across each week and district within that month and governorate; this loses any variation between weeks or between the districts within a given governorate that may have affected incidence. Such spatial and temporal smoothing may obscure differences relevant to disease transmission and thus bias our results. However, it reflects our desire to capture displacement levels relative to districts over a multiyear time series, and we anticipate that this did not severely impact our results. However, future work that looks at these relationships (across environmental factors, conflict, and displacement) at smaller spatial scales would be important should those data become more readily accessible. Lastly, there may be other relevant variables within the causal chain, such as socioeconomic factors and conditions of displacement settlements, that would likely impact disease dynamics but that we were unable to capture.

    The December 2024 fall of the Assad regime and subsequent establishment of a transitional government has undoubtedly moved Syria into a new era. Crucial to this transition will be the creation of robust and resilient healthcare and surveillance systems that can respond to the complex health needs of a population subjected to over a decade of violent conflict. This also presents a rare opportunity to plan for Syria’s future, which can include emerging strategies relating to climate change and disease prevention. Raleigh et al. (2024) have explored the possibility of climate change adaptation interventions relating to natural resource management, climate-smart agriculture, and drought-control measures, underpinned by community-led initiatives and local governance that could be appropriate for fragile, conflict and violence-affected countries [51]. In Syria, this can be informed by research that focuses on evaluating the forecasting capacity of environmental prediction variables, permitting anticipatory action for epidemic prevention according to long-term changes in climate variables and acute climate-related events [52].

    Preparedness efforts that focus on climate change adaptation, with an emphasis on those relating to precipitation and temperature changes, will be important in this region. Syria currently does not have a formal climate change adaptation strategy [53], and the implementation of any future adaptation measures would require a revitalization of international financial support for the region [54]. Increased infectious disease risks are only one of several expected deleterious outcomes of these interplays. Malnutrition, livelihood generation, and the availability of habitable land have already been impacted by climate change and will continue to be so; each of these will also have direct impacts on individuals’ disease susceptibility. Though integrating climate considerations amidst ongoing violent conflict – and now a transitioning government – is certainly not ideal, it is necessary [55]. Such actions, in conjunction with ongoing advocacy and accountability efforts related to conflict, can work toward reducing the suffering experienced by Syrians, many of whom have only known these conditions.

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  • An open dataset and machine learning algorithms for Niacin Skin-Flushing Response based screening of psychiatric disorders | BMC Psychiatry

    An open dataset and machine learning algorithms for Niacin Skin-Flushing Response based screening of psychiatric disorders | BMC Psychiatry

    The data analysis pipeline was bifurcated into two primary stages: precise NSR area detection via Deep Learning-based segmentation, followed by Machine Learning (ML) classification for psychiatric disorder screening.

    Image pre-processing and data augmentation

    To prepare the raw NSR images for analysis and enhance model performance, a series of pre-processing and augmentation steps were applied. Initially, color correction was performed using the Perfect Reflector Method, leveraging the white section of the included label as a standardized white reference to mitigate color inconsistencies arising from diverse acquisition devices and varying lighting conditions. Concurrently, an image size normalization step was executed by utilizing the consistent real-life size of the arm label as a reference, thereby compensating for variations in camera-to-arm distances across different tests. After these initial corrections, all images were uniformly resized to 512 × 128 pixels, a rectangular aspect ratio optimized for the input requirements of the subsequent Deep Learning models.

    To significantly bolster the robustness and generalization capabilities of our segmentation models, runtime data augmentation was extensively implemented during the training phase using the Albumentations library (https://albumentations.ai/). This dynamic augmentation strategy involved applying various transformations such as random rotations, shifts, scaling, flips (horizontal/vertical), and controlled adjustments to brightness, contrast, and saturation. This approach allowed the models to learn more invariant and discriminative features, reducing overfitting and improving performance on unseen data.

    NSR area detection (segmentation)

    For the critical task of identifying and segmenting the subtle NSR areas, which often exhibit low contrast and indistinct boundaries, an advanced Deep Learning approach was adopted. This project explored multiple state-of-the-art segmentation models to identify the most effective architecture for the task. We chose the following Deep Learning models Vit-Unet, Resnet152-Unet, Effb5-Unet, Mob-Deeplab, Mob-DeeplabPlus, Resnet152-Unet++, and plain UNet because they are encoder-decoder based semantic segmentation models known for efficiency and robust performance in medical image analysis, particularly with limited computational resources and data. Their proven capabilities in producing dense pixel-wise predictions, their relative computational efficiency for training on a new dataset, and their interpretability in identifying precise regions made them suitable for our initial objective of establishing a robust and efficient baseline.

    We remark that while Mask R-CNN and DeepLabV3 + are prominent architectures in the field of computer vision, they were not the primary focus for this particular segmentation task for a few key reasons. Mask R-CNN, primarily an instance segmentation model, is optimized for detecting and segmenting individual objects within an image. Our task, however, is a semantic segmentation problem, where we are interested in identifying a single, continuous region (the flushed area) rather than individual instances. Applying Mask R-CNN directly would be an overkill and potentially less efficient for this specific problem type, possibly leading to unnecessary computational overhead and complexity in post-processing to consolidate instances into a single semantic region. DeepLabV3+, on the other hand, is a powerful semantic segmentation model but often leverages dilated convolutions and large receptive fields, which can be computationally intensive and might require larger datasets or more aggressive training strategies to generalize effectively on fine-grained segmentation tasks like NSR, especially given the subtle boundaries. Our objective is to establish a robust and efficient baseline, making encoder-decoder architectures, particularly U-Net variants known for their efficacy in biomedical image segmentation, more suitable for a direct comparative evaluation on our curated dataset.

    Among the evaluated models, the Efficient-Unet (Effb5-Unet) architecture consistently demonstrated superior performance in accurately segmenting the NSR areas from the corrected and augmented images. The comprehensive dataset was divided into distinct sets for training, validation, and testing at the patient level, ensuring that no patient’s data appeared in more than one set. Since there are 120 unique participants, this rigorous partitioning involved 90 of them for training, 10 for validation, and 20 for independent testing. Models were trained using standard optimization protocols, with performance monitored via common metrics including Dice coefficient and Intersection over Union (IoU). We employed no post-processing since the results were good enough and we wanted to directly compare the Deep Learning models only.

    NSR quantification and screening approaches

    Following the NSR area detection, we explored various methods to quantify the NSR and subsequently screen for psychiatric disorders. Previous studies have utilized a 4-point scale for measuring NSR degrees. Recognizing the inherent subjectivity of manual annotation, we developed a more objective 3-scale, derived from the automatically detected NSR areas and their correspondence to manually assigned scores (see the following). Besides this score-based approach, we also applied a direct screening method utilizing the raw detected NSR areas, bypassing discrete score assignment. A comparative analysis of these methods was performed to corroborate the efficacy of our proposed objective quantification.

    NSR area scoring (objective 3-scale)

    For each detected region, we calculate the normalized area, Anorm​, as the ratio of the detected NSR area Adetected​ to the corresponding labeled area Alabel​ (the area of the label attached to the arm), i.e.,

    Anorm​ = Adetected/​​ Alabel.

    We then analyzed the mean value, variance, and standard deviation of the normalized area distributions corresponding to each human score. The observed results were as follows: For score 0, the mean value, variance, and the standard deviation were 0.0648, 0.0229, and 0.1515, respectively. For score 1, they were 0.1535, 0.0363, and 0.1907, respectively. For score 2, they were 0.1661, 0.0217, and 0.1477, respectively. And for score 3, they were 0.1665, 0.0291, and 0.1706, respectively. While the distributions of the normalized areas vary across different scores, there is a considerable overlap and variance. Consequently, we established the next objective 3-scale scoring system based on the above observations:

    Score 0 if Anorm < 0.1091; Score 1 if 0.1091 ≤ Anorm < 0.1598; and Score 2 otherwise.

    Feature extraction for classfication

    For each participant, based on the NSR areas detected by the Efficient-Unet model, we created two distinct types of 20-dimensional feature vectors for classification, each capturing the scores of normalized sizes of the four NSR areas at five critical time points (1st, 5th, 10th, 15th, and 20th minute) post-application. Firstly, we established the objective 3-scale score-based feature vector as explained before. Each element represented the objectively derived 3-scale score (0, 1, or 2) for a specific niacin patch concentration at a given time point. Secondly, we established the direct NSR area feature vector. Each element represented the normalized NSR area Anorm directly for a specific niacin patch concentration at a given time point. This vector comprehensively captured the dynamic and concentration-dependent physiological response with high granularity and objectivity, without discrete score assignment.

    Psychiatric disorder screening (classification)

    The final stage of the analysis involved classifying participants into specific diagnostic groups based on their extracted NSR feature vectors. A Support Vector Machine (SVM), a robust supervised learning model well-suited for high-dimensional data, was selected for this classification task. To ensure the SVM’s performance was optimized and robust against potential biases, several advanced techniques were employed.

    5-Fold cross-validation

    The entire dataset of feature vectors was subjected to 5-fold cross-validation. In each iteration, 80% of the data served as the training set, and the remaining 20% as the test set, ensuring a comprehensive and reliable evaluation of the model’s generalization capabilities. This process was iteratively performed until each subset had been used as the test set once.

    SMOTE for class imbalance

    Given the inherent class imbalances often present in clinical datasets, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to the training data within each cross-validation fold. This algorithm synthesized new minority class samples, thereby balancing the class distribution and preventing the SVM from being unduly biased towards the majority healthy control group.

    Hyperparameter tuning

    Extensive grid search was performed within each cross-validation fold to ascertain the optimal hyperparameters for the SVM model (e.g., kernel function (radial basis function was generally preferred), ‘C’ regularization parameter, and ‘gamma’ for RBF kernel). This meticulous tuning aimed to maximize the balanced accuracy of the classifier, a crucial metric for imbalanced datasets, thereby pushing the SVM’s predictive performance to its empirical limit.

    The final classification performance was evaluated using standard metrics including overall accuracy, as well as class-specific precision, recall (sensitivity), and specificity, across various binary classification tasks (e.g., HC vs. Depression, HC vs. Schizophrenia, HC vs. Bipolar Disorder).

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  • A Rare Case of Giant Esophageal Leiomyoma in an Adult Male Presenting With Right Upper Quadrant Pain and Mild Dysphagia

    A Rare Case of Giant Esophageal Leiomyoma in an Adult Male Presenting With Right Upper Quadrant Pain and Mild Dysphagia


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