A comparative analysis of RNA-Seq and NanoString technologies in deciphering viral infection response in upper airway lung organoids.

In this study, we delved into the comparative analysis of gene expression data across RNA-Seq and NanoString platforms. While RNA-Seq covered 19,671 genes and NanoString targeted 773 genes associated with immune responses to viruses, our primary focus was on the 754 genes found in both platforms. Our experiment involved 16 different infection conditions, with samples derived from 3D airway organ-tissue equivalents subjected to three virus types, influenza A virus (IAV), human metapneumovirus (MPV), and parainfluenza virus 3 (PIV3). Post-infection measurements, after UV (inactive virus) and Non-UV (active virus) treatments, were recorded at 24-h and 72-h intervals. Including untreated and Mock-infected OTEs as control groups enabled differentiating changes induced by the virus from those arising due to procedural elements. Through a series of methodological approaches (including Spearman correlation, Distance correlation, Bland-Altman analysis, Generalized Linear Models Huber regression, the Magnitude-Altitude Score (MAS) algorithm and Gene Ontology analysis) the study meticulously contrasted RNA-Seq and NanoString datasets. The Magnitude-Altitude Score algorithm, which integrates both the amplitude of gene expression changes (magnitude) and their statistical relevance (altitude), offers a comprehensive tool for prioritizing genes based on their differential expression profiles in specific viral infection conditions. We observed a strong congruence between the platforms, especially in identifying key antiviral defense genes. Both platforms consistently highlighted genes including ISG15, MX1, RSAD2, and members of the OAS family (OAS1, OAS2, OAS3). The IFIT proteins (IFIT1, IFIT2, IFIT3) were emphasized for their crucial role in counteracting viral replication by both platforms. Additionally, CXCL10 and CXCL11 were pinpointed, shedding light on the organ tissue equivalent's innate immune response to viral infections. While both platforms provided invaluable insights into the genetic landscape of organoids under viral infection, the NanoString platform often presented a more detailed picture in situations where RNA-Seq signals were more subtle. The combined data from both platforms emphasize their joint value in advancing our understanding of viral impacts on lung organoids.

B cells in perivascular and peribronchiolar granuloma-associated lymphoid tissue and B-cell signatures identify asymptomatic Mycobacterium tuberculosis lung infection in Diversity Outbred mice.

Because most humans resist Mycobacterium tuberculosis infection, there is a paucity of lung samples to study. To address this gap, we infected Diversity Outbred mice with M. tuberculosis and studied the lungs of mice in different disease states. After a low-dose aerosol infection, progressors succumbed to acute, inflammatory lung disease within 60 days, while controllers maintained asymptomatic infection for at least 60 days, and then developed chronic pulmonary tuberculosis (TB) lasting months to more than 1 year. Here, we identified features of asymptomatic M. tuberculosis infection by applying computational and statistical approaches to multimodal data sets. Cytokines and anti-M. tuberculosis cell wall antibodies discriminated progressors vs controllers with chronic pulmonary TB but could not classify mice with asymptomatic infection. However, a novel deep-learning neural network trained on lung granuloma images was able to accurately classify asymptomatically infected lungs vs acute pulmonary TB in progressors vs chronic pulmonary TB in controllers, and discrimination was based on perivascular and peribronchiolar lymphocytes. Because the discriminatory lesion was rich in lymphocytes and CD4 T cell-mediated immunity is required for resistance, we expected CD4 T-cell genes would be elevated in asymptomatic infection. However, the significantly different, highly expressed genes were from B-cell pathways (e.g., Bank1, Cd19, Cd79, Fcmr, Ms4a1, Pax5, and H2-Ob), and CD20+ B cells were enriched in the perivascular and peribronchiolar regions of mice with asymptomatic M. tuberculosis infection. Together, these results indicate that genetically controlled B-cell responses are important for establishing asymptomatic M. tuberculosis lung infection.

Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging.

BACKGROUND AND OBJECTIVES: Deep learning models (DLMs) are applied across domains of health sciences to generate meaningful predictions. DLMs make use of neural networks to generate predictions from discrete data inputs. This study employs DLM on prechemotherapy cross-sectional imaging to predict patients' response to neoadjuvant chemotherapy. METHODS: Adult patients with colorectal liver metastasis who underwent surgery after neoadjuvant chemotherapy were included. A DLM was trained on computed tomography images using attention-based multiple-instance learning. A logistic regression model incorporating clinical parameters of the Fong clinical risk score was used for comparison. Both model performances were benchmarked against the Response Evaluation Criteria in Solid Tumors criteria. A receiver operating curve was created and resulting area under the curve (AUC) was determined. RESULTS: Ninety-five patients were included, with 33,619 images available for study inclusion. Ninety-five percent of patients underwent 5-fluorouracil-based chemotherapy with oxaliplatin and/or irinotecan. Sixty percent of the patients were categorized as chemotherapy responders (30% reduction in tumor diameter). The DLM had an AUC of 0.77. The AUC for the clinical model was 0.41. CONCLUSIONS: Image-based DLM for prediction of response to neoadjuvant chemotherapy in patients with colorectal cancer liver metastases was superior to a clinical-based model. These results demonstrate potential to identify nonresponders to chemotherapy and guide select patients toward earlier curative resection.

Few-shot Tumor Bud Segmentation Using Generative Model in Colorectal Carcinoma.

Current deep learning methods in histopathology are limited by the small amount of available data and time consumption in labeling the data. Colorectal cancer (CRC) tumor budding quantification performed using H&E-stained slides is crucial for cancer staging and prognosis but is subject to labor-intensive annotation and human bias. Thus, acquiring a large-scale, fully annotated dataset for training a tumor budding (TB) segmentation/detection system is difficult. Here, we present a DatasetGAN-based approach that can generate essentially an unlimited number of images with TB masks from a moderate number of unlabeled images and a few annotated images. The images generated by our model closely resemble the real colon tissue on H&E-stained slides. We test the performance of this model by training a downstream segmentation model, UNet++, on the generated images and masks. Our results show that the trained UNet++ model can achieve reasonable TB segmentation performance, especially at the instance level. This study demonstrates the potential of developing an annotation-efficient segmentation model for automatic TB detection and quantification.

Adapting SAM to Histopathology Images for Tumor Bud Segmentation in Colorectal Cancer.

Colorectal cancer (CRC) is the third most common cancer in the United States. Tumor Budding (TB) detection and quantification are crucial yet labor-intensive steps in determining the CRC stage through the analysis of histopathology images. To help with this process, we adapt the Segment Anything Model (SAM) on the CRC histopathology images to segment TBs using SAM-Adapter. In this approach, we automatically take task-specific prompts from CRC images and train the SAM model in a parameter-efficient way. We compare the predictions of our model with the predictions from a trained-from-scratch model using the annotations from a pathologist. As a result, our model achieves an intersection over union (IoU) of 0.65 and an instance-level Dice score of 0.75, which are promising in matching the pathologist's TB annotation. We believe our study offers a novel solution to identify TBs on H&E-stained histopathology images. Our study also demonstrates the value of adapting the foundation model for pathology image segmentation tasks.

Enhancing Colorectal Cancer Tumor Bud Detection Using Deep Learning from Routine H&E-Stained Slides.

Tumor budding refers to a cluster of one to four tumor cells located at the tumor-invasive front. While tumor budding is a prognostic factor for colorectal cancer, counting and grading tumor budding are time consuming and not highly reproducible. There could be high inter- and intra-reader disagreement on H&E evaluation. This leads to the noisy training (imperfect ground truth) of deep learning algorithms, resulting in high variability and losing their ability to generalize on unseen datasets. Pan-cytokeratin staining is one of the potential solutions to enhance the agreement, but it is not routinely used to identify tumor buds and can lead to false positives. Therefore, we aim to develop a weakly-supervised deep learning method for tumor bud detection from routine H&E-stained images that does not require strict tissue-level annotations. We also propose Bayesian Multiple Instance Learning (BMIL) that combines multiple annotated regions during the training process to further enhance the generalizability and stability in tumor bud detection. Our dataset consists of 29 colorectal cancer H&E-stained images that contain 115 tumor buds per slide on average. In six-fold cross-validation, our method demonstrated an average precision and recall of 0.94, and 0.86 respectively. These results provide preliminary evidence of the feasibility of our approach in improving the generalizability in tumor budding detection using H&E images while avoiding the need for non-routine immunohistochemical staining methods.

A nanocomposite competent to overcome solubility and permeation issues of capsaicin and thiocolchicoside simultaneously in gout management: Fabrication of nanocubosomes.

This study aimed to formulate nano-cubosomes (NCs) co-loaded with capsaicin (CAP) and thiocolchicoside (TCS) to enhance their bioavailability and minimize associated potential side effects through transdermal delivery alongside their synergistic activity. Twenty seven (27) nano-cubosomal dispersions were prepared according to Box-Behnken factorial design and the effect of CAP, TCS, glyceryl mono oleate (GMO) and poloxamer 407 (P407) concentrations on particle size, polydispersity index (PDI), zeta potential, and entrapment efficiency were assessed. The results revealed that the optimized formulation exhibited a mean droplet size of 503 ± 10.3 nm, PDI of 0.405 ± 0.02, zeta potential of -10.0 ± 1.70 mV and entrapment efficiency of 86.9 ± 3.56 %. The in vivo anti-inflammatory effect of optimized formulation was studied in rats by injecting carrageenan to induce edema. The results of in vivo study showed that transdermal application of nano-cubosomes co-loaded with CAP and TCS significantly (p value < 0.05) improved carrageenan induced inflammation compared with standard treatment. The analgesic activity of optimized formulation was evaluated in rats by using Eddy's hot plate method. The findings of analgesic activity illustrated that the analgesic effects exhibited by test formulation may be associated with increased licking period and inhibition of prostaglandins level. In conclusion, the transdermal application of NCs co-loaded with CAP and TCS may be a promising delivery system for enhancing their bioavailability as well as synergistic analgesic and anti-inflammatory activity in gout management.

Translating prognostic quantification of c-MYC and BCL2 from tissue microarrays to whole slide images in diffuse large B-cell lymphoma using deep learning.

BACKGROUND: c-MYC and BCL2 positivity are important prognostic factors for diffuse large B-cell lymphoma. However, manual quantification is subject to significant intra- and inter-observer variability. We developed an automated method for quantification in whole-slide images of tissue sections where manual quantification requires evaluating large areas of tissue with possibly heterogeneous staining. We train this method using annotations of tumor positivity in smaller tissue microarray cores where expression and staining are more homogeneous and then translate this model to whole-slide images. METHODS: Our method applies a technique called attention-based multiple instance learning to regress the proportion of c-MYC-positive and BCL2-positive tumor cells from pathologist-scored tissue microarray cores. This technique does not require annotation of individual cell nuclei and is trained instead on core-level annotations of percent tumor positivity. We translate this model to scoring of whole-slide images by tessellating the slide into smaller core-sized tissue regions and calculating an aggregate score. Our method was trained on a public tissue microarray dataset from Stanford and applied to whole-slide images from a geographically diverse multi-center cohort produced by the Lymphoma Epidemiology of Outcomes study. RESULTS: In tissue microarrays, the automated method had Pearson correlations of 0.843 and 0.919 with pathologist scores for c-MYC and BCL2, respectively. When utilizing standard clinical thresholds, the sensitivity/specificity of our method was 0.743 / 0.963 for c-MYC and 0.938 / 0.951 for BCL2. For double-expressors, sensitivity and specificity were 0.720 and 0.974. When translated to the external WSI dataset scored by two pathologists, Pearson correlation was 0.753 & 0.883 for c-MYC and 0.749 & 0.765 for BCL2, and sensitivity/specificity was 0.857/0.991 & 0.706/0.930 for c-MYC, 0.856/0.719 & 0.855/0.690 for BCL2, and 0.890/1.00 & 0.598/0.952 for double-expressors. Survival analysis demonstrates that for progression-free survival, model-predicted TMA scores significantly stratify double-expressors and non double-expressors (p = 0.0345), whereas pathologist scores do not (p = 0.128). CONCLUSIONS: We conclude that proportion of positive stains can be regressed using attention-based multiple instance learning, that these models generalize well to whole slide images, and that our models can provide non-inferior stratification of progression-free survival outcomes.

Association of CT-Derived Skeletal Muscle and Adipose Tissue Metrics with Frailty in Older Adults.

RATIONALE AND OBJECTIVES: Tools are needed for frailty screening of older adults. Opportunistic analysis of body composition could play a role. We aim to determine whether computed tomography (CT)-derived measurements of muscle and adipose tissue are associated with frailty. MATERIALS AND METHODS: Outpatients aged ≥ 55 years consecutively imaged with contrast-enhanced abdominopelvic CT over a 3-month interval were included. Frailty was determined from the electronic health record using a previously validated electronic frailty index (eFI). CT images at the level of the L3 vertebra were automatically segmented to derive muscle metrics (skeletal muscle area [SMA], skeletal muscle density [SMD], intermuscular adipose tissue [IMAT]) and adipose tissue metrics (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT]). Distributions of demographic and CT-derived variables were compared between sexes. Sex-specific associations of muscle and adipose tissue metrics with eFI were characterized by linear regressions adjusted for age, race, ethnicity, duration between imaging and eFI measurements, and imaging parameters. RESULTS: The cohort comprised 886 patients (449 women, 437 men, mean age 67.9 years), of whom 382 (43%) met the criteria for pre-frailty (ie, 0.10 < eFI ≤ 0.21) and 138 (16%) for frailty (eFI > 0.21). In men, 1 standard deviation changes in SMD (ß = -0.01, 95% confidence interval [CI], -0.02 to -0.001, P = .02) and VAT area (ß = 0.008, 95% CI, 0.0005-0.02, P = .04), but not SMA, IMAT, or SAT, were associated with higher frailty. In women, none of the CT-derived muscle or adipose tissue metrics were associated with frailty. CONCLUSION: We observed a positive association between frailty and CT-derived biomarkers of myosteatosis and visceral adiposity in a sex-dependent manner.

Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images.

Multiple instance learning (MIL) models have achieved remarkable success in analyzing whole slide images (WSIs) for disease classification problems. However, with regard to giga-pixel WSI classification problems, current MIL models are often incapable of differentiating a WSI with extremely small tumor lesions. This minute tumor-to-normal area ratio in a MIL bag inhibits the attention mechanism from properly weighting the areas corresponding to minor tumor lesions. To overcome this challenge, we propose salient instance inference MIL (SiiMIL), a weakly-supervised MIL model for WSI classification. We introduce a novel representation learning for histopathology images to identify representative normal keys. These keys facilitate the selection of salient instances within WSIs, forming bags with high tumor-to-normal ratios. Finally, an attention mechanism is employed for slide-level classification based on formed bags. Our results show that salient instance inference can improve the tumor-to-normal area ratio in the tumor WSIs. As a result, SiiMIL achieves 0.9225 AUC and 0.7551 recall on the Camelyon16 dataset, which outperforms the existing MIL models. In addition, SiiMIL can generate tumor-sensitive attention heatmaps that is more interpretable to pathologists than the widely used attention-based MIL method. Our experiments imply that SiiMIL can accurately identify tumor instances, which could only take up less than 1% of a WSI, so that the ratio of tumor to normal instances within a bag can increase by two to four times.

The correlation between rheological properties and extrusion-based printability in bioink artifact quantification.

Bioinks for cell-based bioprinting face availability limitations. Furthermore, the bioink development process needs comprehensive printability assessment methods and a thorough understanding of rheological factors' influence on printing outcomes. To bridge this gap, our study aimed to investigate the relationship between rheological properties and printing outcomes. We developed a specialized bioink artifact specifically designed to improve the quantification of printability assessment. This bioink artifact adhered to established criteria from extrusion-based bioprinting approaches. Seven hydrogel-based bioinks were selected and tested using the bioink artifact and rheological measurement. Rheological analysis revealed that the high-performing bioinks exhibited notable characteristics such as high storage modulus, low tan(δ), high shear-thinning capabilities, high yield stress, and fast, near-complete recovery abilities. Although rheological data alone cannot fully explain printing outcomes, certain metrics like storage modulus and tan(δ) correlated well (R2 > 0.9) with specific printing outcomes, such as gap-spanning capability and turn accuracy. This study provides a comprehensive examination of bioink shape fidelity across a wide range of bioinks, rheological measures, and printing outcomes. The results highlight the importance of considering the holistic view of bioink's rheological properties and directly measuring printing outcomes. These findings underscore the need to enhance bioink availability and establish standardized methods for assessing printability.

One label is all you need: Interpretable AI-enhanced histopathology for oncology.

Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that require only one overall label per hematoxylin and eosin (H&E) slide with no tissue-level annotations. We present a structured review of these methods organized by their degree of verifiability and by commonly recurring application areas in oncological characterization. First, we discuss morphological markers (tumor presence/absence, metastases, subtypes, grades) in which AI-identified regions of interest (ROIs) within whole slide images (WSIs) verifiably overlap with pathologist-identified ROIs. Second, we discuss molecular markers (gene expression, molecular subtyping) that are not verified via H&E but rather based on overlap with positive regions on adjacent tissue. Third, we discuss genetic markers (mutations, mutational burden, microsatellite instability, chromosomal instability) that current technologies cannot verify if AI methods spatially resolve specific genetic alterations. Fourth, we discuss the direct prediction of survival to which AI-identified histopathological features quantitatively correlate but are nonetheless not mechanistically verifiable. Finally, we discuss in detail several opportunities and challenges for these one-label-per-slide methods within oncology. Opportunities include reducing the cost of research and clinical care, reducing the workload of clinicians, personalized medicine, and unlocking the full potential of histopathology through new imaging-based biomarkers. Current challenges include explainability and interpretability, validation via adjacent tissue sections, reproducibility, data availability, computational needs, data requirements, domain adaptability, external validation, dataset imbalances, and finally commercialization and clinical potential. Ultimately, the relative ease and minimum upfront cost with which relevant data can be collected in addition to the plethora of available AI methods for outcome-driven analysis will surmount these current limitations and achieve the innumerable opportunities associated with AI-driven histopathology for the benefit of oncology.

Predicting obstructive sleep apnea severity from craniofacial images using ensemble machine learning models.

Obstructive sleep apnea (OSA) is a prevalent disease affecting 10 to 15% of Americans and nearly one billion people worldwide. It leads to multiple symptoms including daytime sleepiness; snoring, choking, or gasping during sleep; fatigue; headaches; non-restorative sleep; and insomnia due to frequent arousals. Although polysomnography (PSG) is the gold standard for OSA diagnosis, it is expensive, not universally available, and time-consuming, so many patients go undiagnosed due to lack of access to the test. Given the incomplete access and high cost of PSG, many studies are seeking alternative diagnosis approaches based on different data modalities. Here, we propose a machine learning model to predict OSA severity from 2D frontal view craniofacial images. In a cross-validation study of 280 patients, our method achieves an average AUC of 0.780. In comparison, the craniofacial analysis model proposed by a recent study only achieves 0.638 AUC on our dataset. The proposed model also outperforms the widely used STOP-BANG OSA screening questionnaire, which achieves an AUC of 0.52 on our dataset. Our findings indicate that deep learning has the potential to significantly reduce the cost of OSA diagnosis.

Motor synchronization and impulsivity in pediatric borderline personality disorder with and without attention-deficit hyperactivity disorder: an eye-tracking study of saccade, blink and pupil behavior.

Shifting motor actions from reflexively reacting to an environmental stimulus to predicting it allows for smooth synchronization of behavior with the outside world. This shift relies on the identification of patterns within the stimulus - knowing when a stimulus is predictable and when it is not - and launching motor actions accordingly. Failure to identify predictable stimuli results in movement delays whereas failure to recognize unpredictable stimuli results in early movements with incomplete information that can result in errors. Here we used a metronome task, combined with video-based eye-tracking, to quantify temporal predictive learning and performance to regularly paced visual targets at 5 different interstimulus intervals (ISIs). We compared these results to the random task where the timing of the target was randomized at each target step. We completed these tasks in female pediatric psychiatry patients (age range: 11-18 years) with borderline personality disorder (BPD) symptoms, with (n = 22) and without (n = 23) a comorbid attention-deficit hyperactivity disorder (ADHD) diagnosis, against controls (n = 35). Compared to controls, BPD and ADHD/BPD cohorts showed no differences in their predictive saccade performance to metronome targets, however, when targets were random ADHD/BPD participants made significantly more anticipatory saccades (i.e., guesses of target arrival). The ADHD/BPD group also significantly increased their blink rate and pupil size when initiating movements to predictable versus unpredictable targets, likely a reflection of increased neural effort for motor synchronization. BPD and ADHD/BPD groups showed increased sympathetic tone evidenced by larger pupil sizes than controls. Together, these results support normal temporal motor prediction in BPD with and without ADHD, reduced response inhibition in BPD with comorbid ADHD, and increased pupil sizes in BPD patients. Further these results emphasize the importance of controlling for comorbid ADHD when querying BPD pathology.

NRK-ABMIL: Subtle Metastatic Deposits Detection for Predicting Lymph Node Metastasis in Breast Cancer Whole-Slide Images.

The early diagnosis of lymph node metastasis in breast cancer is essential for enhancing treatment outcomes and overall prognosis. Unfortunately, pathologists often fail to identify small or subtle metastatic deposits, leading them to rely on cytokeratin stains for improved detection, although this approach is not without its flaws. To address the need for early detection, multiple-instance learning (MIL) has emerged as the preferred deep learning method for automatic tumor detection on whole slide images (WSIs). However, existing methods often fail to identify some small lesions due to insufficient attention to small regions. Attention-based multiple-instance learning (ABMIL)-based methods can be particularly problematic because they may focus too much on normal regions, leaving insufficient attention for small-tumor lesions. In this paper, we propose a new ABMIL-based model called normal representative keyset ABMIL (NRK-ABMIL), which addresseses this issue by adjusting the attention mechanism to give more attention to lesions. To accomplish this, the NRK-ABMIL creates an optimal keyset of normal patch embeddings called the normal representative keyset (NRK). The NRK roughly represents the underlying distribution of all normal patch embeddings and is used to modify the attention mechanism of the ABMIL. We evaluated NRK-ABMIL on the publicly available Camelyon16 and Camelyon17 datasets and found that it outperformed existing state-of-the-art methods in accurately identifying small tumor lesions that may spread over a few patches. Additionally, the NRK-ABMIL also performed exceptionally well in identifying medium/large tumor lesions.

Safety and Efficacy of Istaroxime in Patients With Acute Heart Failure: A Meta-Analysis of Randomized Controlled Trials.

The aim of this study was to assess the efficacy and safety of istaroxime in patients with heart failure. Following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, a search was conducted on the EMBASE and Medline databases to identify articles related to the safety and efficacy of istaroxime in patients with heart failure. The search covered the period from inception to May 31st, 2023, without any restrictions on the year of publication. The search strategy utilized relevant terms such as "istaroxime," "heart failure", "efficacy," and other related terms, along with their corresponding Medical Subject Headings (MeSH) terms. The outcomes assessed in this meta-analysis included the change in left ventricular ejection fraction (LVEF), E to A ratio (a marker of left ventricle function), cardiac index in L/min/m2, systolic blood pressure (SBP) in mmHg, left ventricular end-systolic volume (LVESV) in ml, and left ventricular end-diastolic volume (LVDSV) in ml. For safety analysis, gastrointestinal events and cardiovascular events were assessed. A total of three randomized controlled trials (RCTs) were included in this meta-analysis encompassing 211 patients with heart failure. Pooled analysis showed that istaroxime was effective in increasing LVEF (MD: 1.26, 95% CI: 0.91 to 1.62, p-value: 0.001), reducing E to A ratio (MD: -0.39, 95% CI: -0.60 to -0.19, p-value: 0.001), increasing cardiac index (MD: 0.22, 95% CI: 0.18 to 0.25, p-value: 0.001), reducing LVESV (MD: -11.84, 95% CI: -13.91 to -9.78, p-value: 0.001), reducing LVEDV (MD: -12.25, 95% CI: -14.63 to -9.87, p-value: 0.001) and increasing SBP (MD: 8.41, 95% CI: 5.23 to 11.60, p-value: 0.001) compared to the placebo group. However, risk of gastrointestinal events was significantly higher in patients receiving istaroxime compared to the placebo group (RR: 2.64, 95% CI: 1.53 to 4.57, p-value: 0.0005). These findings support the enhancement of heart function with istaroxime administration, aligning with previous clinical and experimental evidence.

The role of psychiatry in diagnosis and treatment of paediatric chronic fatigue syndrome - a scoping literature review.

OBJECTIVES: Paediatric Chronic Fatigue Syndrome (pCFS) is a common condition that significantly disrupts a healthy psychosocial development. Psychiatric symptoms associated with pCFS are conceptualized as either part of its complex etiology, its consequence, or as a comorbidity. However, patients with this condition are rarely seen by psychiatrists. This scoping review aims to explore the role of psychiatry in the diagnosis and treatment of pCFS. CONTENT: A scoping review of literature was conducted using MEDLINE, EMBASE, Cochrane and PsycINFO. Databases were searched for articles describing psychiatric involvement in the diagnosis or treatment of children and adolescents (age ≤ 18) with pCFS. A grey literature search was also conducted to identify additional guidelines and national recommendations to identify the role of psychiatry in the diagnosis and treatment of pCFS. SUMMARY: The search provided 436 articles of which 16 met inclusion criteria. Grey literature search identified 12 relevant guidelines. Most studies and guidelines did not include any psychiatric involvement in the care of patients with pCFS. If psychiatry was mentioned, it was used interchangeably with psychological interventions or in the context of treating distinct psychiatric comorbidities and suicidal ideation. OUTLOOK: The role of psychiatry in diagnosis and treatment of pCFS is poorly defined. Future research is required to understand how psychiatrists can contribute to the care of patients with pCFS.

Eye-tracking in adult depression: protocol for a systematic review and meta-analysis.

INTRODUCTION: In recent years, eye-tracking has been proposed as a promising tool to identify potential biomarkers for mental disorders, including major depression. We will conduct an updated systematic review and meta-analysis on eye-tracking research in adults with major depressive disorder or other clinically diagnosed depressive disorders. METHODS AND ANALYSIS: This protocol follows all reporting items in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol extension. We will conduct a systematic search of PubMed, PsycINFO, Google Scholar and EMBASE for sources published up until March 2023. Abstract and full-text review will be completed independently by two reviewers. Non-randomised studies using eye movement tasks in individuals with a depressive disorder versus controls will be included. Eye movement tasks of interest include, but are not limited to, saccade, smooth pursuit, fixation, free-viewing, attentional disengagement, visual search and attentional blink tasks. Results will be categorised by eye movement task. Risk of bias will be assessed using the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies and confidence in cumulative evidence will be assessed using Grading of Recommendations, Assessment, Development and Evaluation criteria. ETHICS AND DISSEMINATION: Ethics approval is not required due to the nature of the proposed analysis. Results will be disseminated through a journal article, conference presentations and/or dissertations.

Machine learning-based analytics of the impact of the Covid-19 pandemic on alcohol consumption habit changes among United States healthcare workers.

The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States.

BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images.

Breast cancer is the most common malignancy in women, with over 40,000 deaths annually in the United States alone. Clinicians often rely on the breast cancer recurrence score, Oncotype DX (ODX), for risk stratification of breast cancer patients, by using ODX as a guide for personalized therapy. However, ODX and similar gene assays are expensive, time-consuming, and tissue destructive. Therefore, developing an AI-based ODX prediction model that identifies patients who will benefit from chemotherapy in the same way that ODX does would give a low-cost alternative to the genomic test. To overcome this problem, we developed a deep learning framework, Breast Cancer Recurrence Network (BCR-Net), which automatically predicts ODX recurrence risk from histopathology slides. Our proposed framework has two steps. First, it intelligently samples discriminative features from whole-slide histopathology images of breast cancer patients. Then, it automatically weights all features through a multiple instance learning model to predict the recurrence score at the slide level. On a dataset of H&E and Ki67 breast cancer resection whole slides images (WSIs) from 99 anonymized patients, the proposed framework achieved an overall AUC of 0.775 (68.9% and 71.1% accuracies for low and high risk) on H&E WSIs and overall AUC of 0.811 (80.8% and 79.2% accuracies for low and high risk) on Ki67 WSIs of breast cancer patients. Our findings provide strong evidence for automatically risk-stratify patients with a high degree of confidence. Our experiments reveal that the BCR-Net outperforms the state-of-the-art WSI classification models. Moreover, BCR-Net is highly efficient with low computational needs, making it practical to deploy in limited computational settings.