Physiotherapy Rehabilitation to Recuperate a Patient From an Intertrochanteric Fracture: A Case Report.

Intertrochanteric fracture is a prevalent condition among older adults, and it is becoming more so as the population is aging. A 52-year-old man was reported to the hospital with symptoms of pain and swelling in the right hip since the morning. The patient reported a history of unexpected slips and falls in the morning. An X-ray was taken of both hips, and an intertrochanteric fracture was identified. After one month post-fracture, a dynamic hip screw (DHS) was used to perform open reduction internal fixation (ORIF). Early mobility, appropriate lower limb strength, pain reduction, and quality of life are all significant determinants. As evidenced by statistically significant improvements in exercise capacity and well-being, the intertrochanteric fracture rehabilitation program is beneficial. This case study represents a comprehensive rehabilitation program for people who have had post-fracture surgery.

Factors Determining Tourists Motivation To Visit A Place for Adventure Tourism: A Systematic Review

India is a country with diversity. It is the gift of nature wherein different rivers, mountains, hill stations, forts, caves, historical places are in the country. It is the social, cultural, religious, and geographical diversity. All these conditions are favourable for travel, tourism and hospitality in India. The young generation, use of technology, expert guidance, safety tools have paved the ways to adventure tourism not only in India but also in the world. The ‘adventure tourism’ is a niche form of tourism which includes deep exploration and extensive travelling to far-flung areas. It is based on the principle of ‘expect the unexpected.’ This is because it is closely association with greater risk as compared to other kinds of tourism. Some of the best examples could be rock climbing, mountaineering expedition, trekking, river rafting, Scuba diving etc. The present study is a review article which examines the different trends, challenges, opportunities, and prospects of adventure tourism in India. The systematic literature review method is applied in this regard. The author has used “Indian Tourism”, “Adventure Tourism”, “Adventure Sports” “Trekking”, “Rock Climbing”, “River Rafting”, “Mountaineering” and other relevant keywords are used to search the existing literature. The study covers the period from 2000 to 2022 as selection criteria. It is applicable for the Indian region. The impact of COVID-19 on adventure tourism is also taken into consideration. The study is unique and significant as it deals with the regional balance, eco-tourism, sustainable development etc. It has historical, cultural, geographical, socio-economic importance from national and international perspectives.

Deep Learning-Aided Automated Pneumonia Detection and Classification Using CXR Scans.

The COVID-19 pandemic has caused a worldwide catastrophe and widespread devastation that reeled almost all countries. The pandemic has mounted pressure on the existing healthcare system and caused panic and desperation. The gold testing standard for COVID-19 detection, reverse transcription-polymerase chain reaction (RT-PCR), has shown its limitations with 70% accuracy, contributing to the incorrect diagnosis that exaggerated the complexities and increased the fatalities. The new variations further pose unseen challenges in terms of their diagnosis and subsequent treatment. The COVID-19 virus heavily impacts the lungs and fills the air sacs with fluid causing pneumonia. Thus, chest X-ray inspection is a viable option if the inspection detects COVID-19-induced pneumonia, hence confirming the exposure of COVID-19. Artificial intelligence and machine learning techniques are capable of examining chest X-rays in order to detect patterns that can confirm the presence of COVID-19-induced pneumonia. This research used CNN and deep learning techniques to detect COVID-19-induced pneumonia from chest X-rays. Transfer learning with fine-tuning ensures that the proposed work successfully classifies COVID-19-induced pneumonia, regular pneumonia, and normal conditions. Xception, Visual Geometry Group 16, and Visual Geometry Group 19 are used to realize transfer learning. The experimental results were promising in terms of precision, recall, F1 score, specificity, false omission rate, false negative rate, false positive rate, and false discovery rate with a COVID-19-induced pneumonia detection accuracy of 98%. Experimental results also revealed that the proposed work has not only correctly identified COVID-19 exposure but also made a distinction between COVID-19-induced pneumonia and regular pneumonia, as the latter is a very common disease, while COVID-19 is more lethal. These results mitigated the concern and overlap in the diagnosis of COVID-19-induced pneumonia and regular pneumonia. With further integrations, it can be employed as a potential standard model in differentiating the various lung-related infections, including COVID-19.

Renal Autologous Cell Therapy to Stabilize Function in Diabetes-Related Chronic Kidney Disease: Corroboration of Mechanistic Action With Cell Marker Analysis.

Introduction: Chronic kidney disease (CKD) is a worldwide disease without cure. Selected renal cells (SRCs) can augment kidney function in animal models. This study correlates the phenotypical characteristics of autologous homologous SRCs (formulated product called Renal Autologous Cell Therapy [REACT]) injected into patients' kidneys with advanced type 2 diabetes-related CKD (D-CKD) to clinical and laboratory findings. Methods: A total of 22 adults with type 2 D-CKD underwent a kidney biopsy followed by 2 subcortical injections of SRCs, 7 ± 3 months apart. There were 2 patients who had only 1 injection. We compared annualized estimated glomerular filtration rate (eGFR) slopes pre- and post-REACT injection using the 2009 CKD-EPI formula for serum creatinine (sCr) and the 2012 CKD-EPI Creatinine-Cystatin C equation and report clinical/laboratory changes. Fluorescent Activated Cell Sorting (FACS) Analysis for renal progenitor lineages in REACT and donor vascular endothelial growth factor A (VEGF-A) analysis were performed. Longitudinal parameter changes were analyzed with longitudinal linear mixed effects model. Results: At baseline, the mean diabetes duration was 18.4 ± 8.80 years, glycated hemoglobin (Hgb) was 7.0 ± 1.05, and eGFR was 40.3 ± 9.35 ml/min per 1.73 m2 using the 2012 CKD-EPI cystatin C and sCr formulas. The annualized eGFR slope (2012 CKD-EPI) was -4.63 ml/min per 1.73 m2 per year pre-injection and improved to -1.69 ml/min per 1.73 m2 per year post-injection (P = 0.015). There were 7 patients who had an eGFR slope of >0 ml/min per 1.73 m2 postinjection. SRCs were found to have cell markers of ureteric bud, mesenchyme cap, and podocyte sources and positive VEGF. There were 2 patients who had remote fatal adverse events determined as unrelated with the biopsies/injections or the REACT product. Conclusion: Our cell marker analysis suggests that SRCs may enable REACT to stabilize and improve kidney function, possibly halting type 2 D-CKD progression.

Integrating patient symptoms, clinical readings, and radiologist feedback with computer-aided diagnosis system for detection of infectious pulmonary disease: a feasibility study.

Automatic computer-aided diagnosis (CAD) system has been widely used as an assisting tool for mass screening and risk assessment of infectious pulmonary diseases (PDs). However, such a system still lacks clinical acceptability and trust due to the integration gap between the patient's metadata, radiologist feedback, and the CAD system. This paper proposed three integration frameworks, namely-direct integration (DI), rule-based integration (RBI), and weight-based integration (WBI). The proposed framework helps clinicians diagnose lung inflammation and provide an end-to-end robust diagnostic system. Initially, the feasibility of integrating patients' symptoms, clinical pathologies, and radiologist feedback with CAD system to improve the classification performance is investigated. Subsequently, the patient's metadata and radiologist feedback are integrated with the CAD system using the proposed integration frameworks. The proposed method's performance is evaluated using a private dataset consisting of 70 chest X-ray (CXR) images (31 COVID-19, 14 other diseases, and 25 normal). The obtained results reveal that the proposed WBI achieved the highest classification performance (accuracy = 98.18%, F1 score = 97.73%, and Matthew's correlation coefficient = 0.969) compared to DI and RI. The generalization capability of the proposed framework is also verified from an external validation set. Furthermore, the Friedman average ranking and Shaffer and Holm post hoc statistical methods reveal the obtained results' statistical significance. Methodological diagram of proposed integration frameworks.

Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification.

BACKGROUND AND OBJECTIVES: Chest X-ray (CXR) is a non-invasive imaging modality used in the prognosis and management of chronic lung disorders like tuberculosis (TB), pneumonia, coronavirus disease (COVID-19), etc. The radiomic features associated with different disease manifestations assist in detection, localization, and grading the severity of infected lung regions. The majority of the existing computer-aided diagnosis (CAD) system used these features for the classification task, and only a few works have been dedicated to disease-localization and severity scoring. Moreover, the existing deep learning approaches use class activation map and Saliency map, which generate a rough localization. This study aims to generate a compact disease boundary, infection map, and grade the infection severity using proposed multistage superpixel classification-based disease localization and severity assessment framework. METHODS: The proposed method uses a simple linear iterative clustering (SLIC) technique to subdivide the lung field into small superpixels. Initially, the different radiomic texture and proposed shape features are extracted and combined to train different benchmark classifiers in a multistage framework. Subsequently, the predicted class labels are used to generate an infection map, mark disease boundary, and grade the infection severity. The performance is evaluated using a publicly available Montgomery dataset and validated using Friedman average ranking and Holm and Nemenyi post-hoc procedures. RESULTS: The proposed multistage classification approach achieved accuracy (ACC)= 95.52%, F-Measure (FM)= 95.48%, area under the curve (AUC)= 0.955 for Stage-I and ACC=85.35%, FM=85.20%, AUC=0.853 for Stage-II using calibration dataset and ACC = 93.41%, FM = 95.32%, AUC = 0.936 for Stage-I and ACC = 84.02%, FM = 71.01%, AUC = 0.795 for Stage-II using validation dataset. Also, the model has demonstrated the average Jaccard Index (JI) of 0.82 and Pearson's correlation coefficient (r) of 0.9589. CONCLUSIONS: The obtained classification results using calibration and validation dataset confirms the promising performance of the proposed framework. Also, the average JI shows promising potential to localize the disease, and better agreement between radiologist score and predicted severity score (r) confirms the robustness of the method. Finally, the statistical test justified the significance of the obtained results.

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble.

Novel coronavirus disease (nCOVID-19) is the most challenging problem for the world. The disease is caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2), leading to high morbidity and mortality worldwide. The study reveals that infected patients exhibit distinct radiographic visual characteristics along with fever, dry cough, fatigue, dyspnea, etc. Chest X-Ray (CXR) is one of the important, non-invasive clinical adjuncts that play an essential role in the detection of such visual responses associated with SARS-COV-2 infection. However, the limited availability of expert radiologists to interpret the CXR images and subtle appearance of disease radiographic responses remains the biggest bottlenecks in manual diagnosis. In this study, we present an automatic COVID screening (ACoS) system that uses radiomic texture descriptors extracted from CXR images to identify the normal, suspected, and nCOVID-19 infected patients. The proposed system uses two-phase classification approach (normal vs. abnormal and nCOVID-19 vs. pneumonia) using majority vote based classifier ensemble of five benchmark supervised classification algorithms. The training-testing and validation of the ACoS system are performed using 2088 (696 normal, 696 pneumonia and 696 nCOVID-19) and 258 (86 images of each category) CXR images, respectively. The obtained validation results for phase-I (accuracy (ACC) = 98.062%, area under curve (AUC) = 0.956) and phase-II (ACC = 91.329% and AUC = 0.831) show the promising performance of the proposed system. Further, the Friedman post-hoc multiple comparisons and z-test statistics reveals that the results of ACoS system are statistically significant. Finally, the obtained performance is compared with the existing state-of-the-art methods.

Air Leak Syndrome in Two Very Preterm Infants Born to Mothers with Coronavirus Disease 2019: An Association or a Coincidence?

The novel coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mostly affects adults with limited information on possible vertical transmission from pregnant mothers. We present here two very preterm infants born to mothers with COVID-19, whose respiratory course was significant for initial mild respiratory distress syndrome who developed acute onset severe air leak syndrome at approximately 24 to 36 hours of age requiring thoracentesis. Their respiratory status improved gradually with resolution of air leak and respiratory failure by 2 weeks of age. Both infants tested negative for SARS-CoV-2 by reverse transcriptase-polymerase chain reaction of multiple respiratory specimens collected beyond 24 hours after birth. As the incidence of severe air leak syndrome is relatively low in preterm infants without risk factors, this presentation in two very preterm infants born to mothers with COVID-19 is intriguing and needs to be further evaluated in larger cohorts. If confirmed, this data could potentially be the first step toward generating hypotheses for mechanisms of lung injury, intrauterine transmission, or how to detect COVID-19 in preterm infants. In addition, these data will be critical for developing evidence-based guidelines for perinatal management of these infants as we continue to battle against the COVID-19 pandemic for the foreseeable future.