Citations

Abstract: The liver is exposed to several harmful substances that bear the potential to cause excessive liver damage ranging from hepatitis and non-alcoholic fatty liver disease to extreme cases of liver cirrhosis and hepatocellular carcinoma. Liver ailments have been effectively treated from very old times with Chinese medicinal herbal formulations and later also applied by controlled trials in Japan. However, these traditional practices have been hardly well characterized in the past till in the last decades when more qualified studies have been carried out. Modern advances have given rise to specific molecular targets which are specifically good candidates for affecting the intricate mechanisms that play a role at the molecular level. These therapeutic regimens that mainly affect the progression of the disease by inhibiting the gene expression levels or by blocking essential molecular pathways or releasing cytokines may prove to play a vital role in minimizing the tissue damage. This review, therefore, tries to throw light upon the variation in the therapies for the treatment of benign and malignant liver disease from ancient times to the current date. Nonetheless, clinical research exploring the effectiveness of herbal medicines in the treatment of benign chronic liver diseases as well as prevention and treatment of HCC is still warranted.

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

Background: The mean length of stay (LOS) after minimally invasive radical prostatectomy (MI-RP) is <2 days. Our main objective was to utilize the National Surgical Quality Improvement Program (NSQIP) database to evaluate preoperative factors that may contribute to prolonged hospital stay and readmission. Materials and Methods: Utilizing the NSQIP database, records for surgery with the Current Procedural Terminology code 55866 (prostatectomy) between 2007 and 2017 were evaluated. Chi-square and t -tests were used to assess the effects of preoperative factors on prolonged LOS and rates of hospital readmission within 30 days. Odds ratios (ORs), p -values, and confidence intervals were determined using multivariable logistic regression. Results: A total of 40,764 patients underwent MI-RP between 2007 and 2017. Of these, 11.7% reported an LOS of >2 days, whereas 3.9% of patients were readmitted to the hospital within 30 days. Preoperative congestive heart failure within 30 days of surgery was shown to be strongly associated with both prolonged LOS (OR = 6.16) and readmission (OR = 3.28). Bleeding requiring transfusion was demonstrated to be the most significant postoperative factor for prolonged LOS (OR = 23.9), whereas unplanned intubation was shown to be the most significant postoperative factor for readmission (OR = 57.1). Body mass index (BMI) >30 was associated with both prolonged LOS and increase in readmission. Conclusions: Upon NSQIP database analysis, cardiopulmonary factors and BMI were demonstrated to have negative impacts on postoperative quality indicators. Patients with comorbidities should be counseled preoperatively concerning their individual risk factors. Mitigation of these factors is important in ensuring optimal outcomes.

Abstract Objective To present the pathologic analysis of female urethral strictures obtained during reconstructive urethroplasty. Methods Nine separate female urethral tissue specimens were obtained during dorsal vaginal graft urethroplasty by a single surgeon (S.P.P.). Samples were serially sectioned and fixed in 10% formalin 6 to 12 hours before routine processing in paraffin blocks. Serial 5-µm sections were subjected to H&E, Masson trichrome, and elastin staining. End point analysis included evaluation for epithelial hyperplasia and cell type, mucosal edema, degree of fibroblast/inflammatory cell infiltrate, and elastin fiber density and distribution. Results Nine specimens were examined. Six specimens had epithelial linings of stratified squamous epithelium overlying fibrosis (67%), 1 had mixed squamous and urothelial epithelium, and 2 had only urothelial epithelium. Two specimens (29%) showed acute injury with prominent squamous papillary hyperplasia, focal erosion, and patchy mucosal hemorrhage. Areas of urethral stricture were variably thickened, with increased, densely packed collagen fibers and associated mucosal lymphocytic inflammation ranging from mild and patchy to focally dense with lymphoid aggregates. The highest elastin fiber density appeared to be associated with vessels and overlying muscle bundles in the submucosa. Conclusions Further elucidation of histopathologic characteristics may illuminate more appropriate therapeutic pathways for female urethral stricture disease management.

Wolframin-1–positive neurons in the entorhinal cortex of mouse brain propagate tau to the hippocampal CA1 region resulting in memory impairment.

Abstract Objective Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs. Materials and Methods A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review. Results Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9). Conclusion NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

Introduction: Phenome-wide association studies (PheWAS) using electronic medical record (EMR)-linked biobanks have been used not only to identify and replicate known associations of genetic variants with disease phenotypes but have also resulted in the discovery of potentially novel genotype-phenotype relationships. The natriuretic peptide (NP) system plays an important role in a broad range of disease processes including cardiovascular and inflammatory diseases. We hypothesized that performing a PheWAS using previously known functional genetic variants of the NP system may result in novel disease associations that could provide mechanistic insights in an unbiased manner. Methods: We scanned for associations between 9 single-nucleotide polymorphisms (SNPs) in the NP system and 27 EMR-derived chronic disease phenotypes in 3,025 individuals participating in a case-control study of peripheral arterial disease. The EMR phenotypes were identified using two or more ICD-9-CM diagnosis codes based on the AHRQ Clinical Classifications Software (CCS). The relationship of SNPs and phenotypes were modeled using logistic regression adjusting for gender. Results: We identified rs5065, a SNP located in the stop codon of exon 3 of the NPPA gene, to be the strongest associated SNP with rheumatoid arthritis (RA) (OR=0.78, p=0.0008, q-value=0.11). The SNP leads to the extension of atrial natriuretic peptide (ANP) by 2 additional arginines at the C terminus. Cardiovascular disease is known to be the leading cause of death in patients with RA and ANP plays an important immunomodulatory role by inhibiting inducible nitric oxide synthase, reducing TNF-α production and attenuating prostaglandin E2 production in macrophages. Circulating NPs have been used to screen for occult cardiac disease and are associated with mortality in RA. This study demonstrates for the first time the importance of the relationship between genetic variation in the NP system and RA. Conclusions: PheWAS was successfully used as a tool to identify a novel association of functional genetic variation in the NPPA gene with RA. The observation is hypothesis generating and further replication studies are required to determine the role of rs5065 in cardiovascular outcomes of RA.

Sarcopenia is a commonly prevalent geriatric condition mainly characterized by progressive loss of the skeletal muscle mass that results in noticeably reduced muscle strength and quality. Most of the geriatric population above 60 years of age are overweight, leading to the accumulation of fat in the muscles resulting in abated muscle function. The increased loss of muscle mass is associated with high rates of disability, poor motility, frailty and mortality. The excessive degeneration of muscles is now also being observed in middle-aged people. Therefore, geriatrics has recently started shifting towards the identification of early stages of the disability in order to expand the life span of the patient and reduce physical dependence. Recent findings have indicated that patients with increased physical activity are also affected by sarcopenia, therefore indicating the role of nutritional supplements to enhance muscle health which in turn helps to counteract sarcopenia. Various interventions with physical training have not provided substantial improvements to this disorder, thereby highlighting the crucial role of nutritional supplementation in enhancing muscle mass and strength. Nutritional supplementation has not only been shown to enhance the positive effects of physical interventions but also have a profound impact on the gut microbiome that has come forward as a key regulator of muscle mass and function. This brief review throws light upon the efficiency of nutrients and nutraceutical supplementation by highlighting their ancillary effects in physical interventions as well as improving the gut microbiome status in sarcopenic adults, thereby giving rise to a multimodal intervention for the treatment of sarcopenia.