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Introduction: Lung transplant (LTx) is a potential treatment option for all patients with chronic, end‑stage respiratory disease, who are refractory to optimal medical therapy or where no medical therapy exists. In India, LTx is still in its evolving stages and published literature is sparse. The current study was carried out to study the selection criteria for lung transplant and to evaluate the clinical and socio‑economic profile of patients referred for the same at a tertiary health care facility. Methods: The study was a descriptive, prospective, observational study. All adults referred for lung transplant were evaluated for clinical and laboratory profiles. All enrolled patients were assessed for presence of referral criteria, listing criteria, contraindications, and willingness for lung transplant. These patients were followed up for 2 years for transplant‑free survival, and the Cox proportional hazards model was used to determine independent predictors of all‑cause mortality. Results: A total of 103 were included in study. The most common diagnosis was interstitial lung disease (57.2%), followed by bronchiectasis (17.5%) and COPD (13.6%). Most patients were referred for LTx at an advanced stage as 90% met listing criteria. Fifty‑four (52.4%) patients had an absolute or relative contraindication to transplant; however, the majority of those contraindications were modifiable. Patients with a lower socio‑economic status were less likely to be willing for LTx. The median survival was 757 days. A 6‑minute walk distance (6MWD) lesser than 250 m was found to be an independent predictor of mortality. Conclusion: Making patients aware about lung transplant early in their treatment may give them sufficient time to come to terms with their disease and understand the risk and benefits associated. Efforts should be focused on screening and early treatment of reversible contraindications for the eligible patients. Patients with 6MWD < 250 m are at increased risk of mortality. [ABSTRACT FROM AUTHOR]

Background: Modalities to improve tissue acquisition during endobronchial ultrasound‑guided transbronchial needle aspiration (EBUS‑TBNA) have been investigated. Endobronchial ultrasound‑guided transbronchial mediastinal cryobiopsy (EBUS‑TMC) is a modality to obtain larger histological samples by inserting a cryoprobe into the mediastinal lesion. We aimed to study the diagnostic yield and safety of EBUS‑TMC. Methods: We performed a systematic search of the PubMed and Embase databases to extract the relevant studies. We then performed a meta‑analysis to calculate the diagnostic yield of EBUS‑TMC and compare it with EBUS‑TBNA. Results: Following a systematic search, we identified 14 relevant studies (869 patients undergoing EBUS‑TMC and EBUS‑TBNA). We then performed a meta‑analysis of the diagnostic yield of EBUS‑TMC and EBUS‑TBNA from studies wherein both procedures were performed. The pooled diagnostic yield of EBUS‑TMC was 92% (95% confidence interval [CI], 89%–95%). The pooled diagnostic yield of EBUS‑TBNA was 81% (95% CI, 77%–85%). The risk difference in yield was 11% (95% CI, 6%–15%, I 2 = 0%) when EBUS‑TMC and EBUS‑TBNA were compared. The only complication reported commonly with EBUS‑TMC was minor bleeding. The complication rate was comparable with EBUS‑TBNA. Conclusion: EBUS‑TMC provides a greater diagnostic yield with a similar risk of adverse events compared to EBUS‑TBNA. Future studies are required to clearly establish which patients are most likely to benefit from this modality. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has affected the world, leading to significant morbidity and mortality. Various meteorological parameters are considered essential for the viability and transmission of the virus. Multiple reports from various parts of the world suggest a correlation between the disease spread and air pollution severity. This study was carried out to identify the relationship between meteorological parameters, air pollution, and COVID-19 in New Delhi, one of the worst-affected states in India. We studied air pollution and meteorological parameters in New Delhi, India. We obtained data about COVID-19 occurrence, meteorological parameters, and air pollution indicators from various sources from April 1, 2020, until November 12, 2020. We performed correlational analysis and employed autoregressive distributed lag models to identify the relationship between COVID-19 cases, air pollution and meteorological parameters. We found a significant impact of particulate matter (PM) 2.5, PM10, and meteorological parameters on COVID-19. There was a significant positive correlation between daily COVID19 cases and COVID-19-related deaths with PM2.5 and PM10 levels. Increasing temperature and wind speed were associated with a reduction in the number of cases, while increasing humidity was associated with increased cases. This study demonstrated a significant association between PM2.5 and PM10 and daily COVID-19 cases and COVID-19-related mortality. This knowledge will likely help us prepare well for the future and implement air pollution control measures for other airborne disease epidemics. [ABSTRACT FROM AUTHOR]

The article presents a case study of a 33-year-old male diagnosed with superior vena cava obstruction (SVCO) caused by mediastinal tuberculosis, a rare benign etiology of this condition. It highlights the diagnostic and therapeutic challenges faced, particularly the successful endovascular stenting procedure combined with anti-tubercular therapy, which resolved the patient's symptoms.

Data represents a compendium of information that perpetually expands with each passing moment, contributed by individuals worldwide. Within the domain of medical science, this reservoir of data accumulates at an almost exponential rate, doubling in volume annually. The emergence of advanced machine learning tools and techniques, subsequent to a substantial evolution in data mining strategies, has bestowed the capacity to glean insights and discern concealed patterns from vast datasets, thus enabling extensive analytical pursuits. This study delves into the application of machine learning algorithms to enhance societal well-being by harnessing the transformative potential of machine learning advancements in the domain of blood glucose concentration estimation through regression analysis. The culmination of this investigation involves establishing a correlation between glucose concentration and hematocrit volume. The dataset employed for this research is sourced from clinically validated electrochemical glucose sensors (commonly referred to as glucose strips). It encompasses diverse levels of both glucose concentration and hematocrit volume, the latter being furnished by an undisclosed source to ensure copyright compliance. This dataset comprises four distinct variables, and the aim of this research involves training the dataset using regression techniques to predict two of these variables. Our results indicate that when utilizing linear regression, the R2 score for GC is approximately 0.916, whereas for HV, it reaches around 0.537. In contrast, employing the support vector regressor yielded R2 scores of about 0.961 for GC and 0.506 for HV. [ABSTRACT FROM AUTHOR]

Background: Mycobacterium w (Mw), an immunomodulator, resulted in better clinical status in severe coronavirus infectious disease 19 (COVID-19) but no survival benefit in a previous study. Herein, we investigate whether Mw could improve clinical outcomes and survival in COVID-19. Materials and Methods: In a multicentric, randomized, double-blind, parallel-group, placebo-controlled trial, we randomized hospitalized subjects with severe COVID-19 to receive either 0.3 mL/day of Mw intradermally or a matching placebo for three consecutive days. The primary outcome was 28-day mortality. The co-primary outcome was the distribution of clinical status assessed on a seven-point ordinal scale ranging from discharged (category 1) to death (category 7) on study days 14, 21, and 28. The key secondary outcomes were the change in sequential organ failure assessment (SOFA) score on days 7 and 14 compared to the baseline, treatment-emergent adverse events, and others. Results: We included 273 subjects (136 Mw, 137 placebo). The use of Mw did not improve 28-day survival (Mw vs. placebo, 18 [13.2%] vs. 12 [8.8%], P = 0.259) or the clinical status on days 14 (odds ratio [OR], 1.33; 95% confidence intervals [CI], 0.79-2.3), 21 (OR, 1.49; 95% CI, 0.83-2.7) or 28 (OR, 1.49; 95% CI, 0.79-2.8) between the two study arms. There was no difference in the delta SOFA score or other secondary outcomes between the two groups. We observed higher injection site reactions with Mw. Conclusion: Mw did not reduce 28-day mortality or improve clinical status on days 14, 21 and 28 compared to placebo in patients with severe COVID-19. [ABSTRACT FROM AUTHOR]

Introduction: The dysregulated host immune response in sepsis is orchestrated by peripheral blood leukocytes. This study explored the associations of the peripheral blood leukocyte subpopulations with early clinical deterioration and mortality in sepsis. Methods: We performed a prospective observational single-center study enrolling adult subjects with sepsis within 48 h of hospital admission. Peripheral blood flow cytometry was performed for the patients at enrolment and after 5 days. The primary outcome was to explore the association between various leukocyte subpopulations at enrolment and early clinical deterioration [defined as an increase in the sequential organ failure assessment (SOFA) score between enrolment and day 5, or death before day 5]. Other pre-specified outcomes explored associations of leukocyte subpopulations at enrolment and on day 5 with in-hospital mortality. Results: A total of 100 patients, including 47 with septic shock were enrolled. The mean (SD) age of the patients was 53.99 (14.93) years. Among them, 26 patients had early clinical deterioration, whereas 41 died during hospitalization. There was no significant association between the leukocyte subpopulations at enrolment and early clinical deterioration on day 5. On multivariate logistic regression, a reduced percentage of CD8 + CD25+ T-cells at enrolment was associated with in-hospital mortality [odds ratio (OR), 0.82 (0.70-0.97); p-value = 0.02]. A reduced lymphocyte percentage on day 5 was associated with in-hospital mortality [OR, 0.28 (0.11-0.69); p-value = 0.01]. In a post-hoc analysis, patients with "very early" deterioration within 48 h had an increased granulocyte CD64 median fluorescent intensity (MFI) [OR, 1.07 (1.01-1.14); p-value = 0.02] and a reduced granulocyte CD16 MFI [OR, 0.97 (0.95-1.00); p-value = 0.04] at enrolment. Conclusions: None of the leukocyte subpopulations showed an association with early clinical deterioration at day 5. Impaired lymphocyte activation and lymphocytopenia indicative of adaptive immune dysfunction may be associated with in-hospital mortality. [ABSTRACT FROM AUTHOR]