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Objective: To develop a reliable scale to measure stress among parents with only daughters. Study Design: Cross-sectional study. Place and Duration of Study: Department of Psychology, University of Gujrat, Gujrat Pakistan, from May to Jul 2019. Methodology: The study was conducted on parents who have only daughters. A pool of 59 items was created through brainstorming sessions and an intensive literature review. Afterwards, 42 items were retained through meticulous evaluation and assessment of experts. Subsequently, a scale was administered to 242 respondents who were selected through a purposive sampling technique. All the items were retained because of their high correlation with item total (r≥0.5). Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) proceeded for structure exploration and confirmation. Furthermore, the reliability of the scale was established. Results: EFA led to five factors encompassing 36 items. Moreover, CFA was carried out, and the model fit summary showed some indices in good ranges (RFI=0.923, NFI=0.940, GFI=0.928) while some in excellent ranges (CFI=0.970, IFI= 0.971, TLI=0.962) after the deletion of 22 items with significance p-value (0.001). The reliability of the scale was 0.93, while the reliability of the subscale ranges from 0.81 to 0.88. Conclusion: A reliable scale to measure Stress among parents having only daughters was successfully developed with 14 items casing four subscales.

Clinical text reports from head computed tomography (CT) represent rich, incompletely utilized information regarding acute brain injuries and neurologic outcomes. CT reports are unstructured; thus, extracting information at scale requires automated natural language processing (NLP). However, designing new NLP algorithms for each individual injury category is an unwieldy proposition. An NLP tool that summarizes all injuries in head CT reports would facilitate exploration of large data sets for clinical significance of neuroradiological findings. To automatically extract acute brain pathological data and their features from head CT reports. This diagnostic study developed a 2-part named entity recognition (NER) NLP model to extract and summarize data on acute brain injuries from head CT reports. The model, termed BrainNERD, extracts and summarizes detailed brain injury information for research applications. Model development included building and comparing 2 NER models using a custom dictionary of terms, including lesion type, location, size, and age, then designing a rule-based decoder using NER outputs to evaluate for the presence or absence of injury subtypes. BrainNERD was evaluated against independent test data sets of manually classified reports, including 2 external validation sets. The model was trained on head CT reports from 1152 patients generated by neuroradiologists at the Yale Acute Brain Injury Biorepository. External validation was conducted using reports from 2 outside institutions. Analyses were conducted from May 2020 to December 2021. Performance of the BrainNERD model was evaluated using precision, recall, and F1 scores based on manually labeled independent test data sets. A total of 1152 patients (mean [SD] age, 67.6 [16.1] years; 586 [52%] men), were included in the training set. NER training using transformer architecture and bidirectional encoder representations from transformers was significantly faster than spaCy. For all metrics, the 10-fold cross-validation performance was 93% to 99%. The final test performance metrics for the NER test data set were 98.82% (95% CI, 98.37%-98.93%) for precision, 98.81% (95% CI, 98.46%-99.06%) for recall, and 98.81% (95% CI, 98.40%-98.94%) for the F score. The expert review comparison metrics were 99.06% (95% CI, 97.89%-99.13%) for precision, 98.10% (95% CI, 97.93%-98.77%) for recall, and 98.57% (95% CI, 97.78%-99.10%) for the F score. The decoder test set metrics were 96.06% (95% CI, 95.01%-97.16%) for precision, 96.42% (95% CI, 94.50%-97.87%) for recall, and 96.18% (95% CI, 95.151%-97.16%) for the F score. Performance in external institution report validation including 1053 head CR reports was greater than 96%. These findings suggest that the BrainNERD model accurately extracted acute brain injury terms and their properties from head CT text reports. This freely available new tool could advance clinical research by integrating information in easily gathered head CT reports to expand knowledge of acute brain injury radiographic phenotypes.

Recent findings suggesting transforming growth factor (TGF)-β1 activation by mechanical stimuli in vitro raised the question of whether this phenomenon was relevant in vivo in the context of pulmonary fibrosis. To explore the effect of mechanical stress on TGF-β1 activation and its signaling pathway in rat and human fibrotic lung tissue using a novel ex vivo model. Rat lung fibrosis was induced using transient gene expression of active TGF-β1. Lungs were harvested at Day 14 or 21 and submitted to various stimuli in a tissue bath equipped with a force transducer and servo-controlled arm. Fibrotic lung strips responded to tensile force by releasing active TGF-β1 from latent stores with subsequent increase in tissue phospho-Smad2/3. In contrast, measurable active TGF-β1 and phospho-Smad2/3 were not induced by mechanical stress in nonfibrotic lungs. Protease inhibition did not affect the release of active TGF-β1. A TGF-β1 receptor inhibitor, Rho-associated protein kinase inhibitor, and αv integrin inhibitor all attenuated mechanical stretch-induced phospho-Smad2/3 in fibrotic lung strips. Furthermore, the induction of phospho-Smad2/3 was enhanced in whole fibrotic rat lungs undergoing ventilation pressure challenge compared with control lungs. Last, tissue slices from human lung with usual interstitial pneumonia submitted to mechanical force showed an increase in TGF-β1 activation and induction of phospho-Smad2/3 in contrast with human nonfibrotic lungs. Mechanical tissue stretch contributes to the development of pulmonary fibrosis via mechanotransduced activation of TGF-β1 in rodent and human pulmonary fibrosis.

Aim This study aims to evaluate the color stability of four provisional materials: polymethyl methacrylate (DPI® Self-Cure), 10-ethoxylated bisphenol A dimethacrylate (Oratemp® C&B), bis-acryl composite resin (Systemp® C&B, Ivoclar Vivadent), and bis-acryl composite (Systemp® C&B, Ivoclar Vivadent) combined with light-cure composite (Fusion Flo® LC). Materials and methods A total of 40 specimens were meticulously crafted from modeling wax into discs, each precisely 2 mm thick and 20 mm in diameter. Four provisional materials were packed into molds, yielding 10 specimens for each material group. After mixing and polymerization, the specimens were trimmed and polished. Reflectance spectrophotometers were used for initial color assessments based on the CIELAB color space system. Staining solutions, including coffee, Tata Green Tea, Pepsi, and turmeric, were prepared to mimic dietary agents. Artificial saliva, replicating oral conditions, was formulated and sterilized. The specimens were then immersed in various solutions for 15 days at 37 °C. Color measurements were taken on days 2 and 15 using the same spectrophotometer, calculating color differences (ΔE) from changes in L*, a*, and b* values. Results DPI Self-Cure (polymethyl methacrylate) was found to be the most color-stable temporary restorative material, followed by Vivadent (bis-acryl composite resin), Oratemp (10-ethoxylated bisphenol A dimethacrylate), and Fusion Flo (light-cure composite). Fusion Flo exhibited the highest color change by the 15th day. Coffee and green tea demonstrated the greatest potential for causing color changes in the provisional restorative materials. Conclusion DPI Self-Cure exhibited the highest color stability among the provisional materials, with Vivadent and Oratemp following closely behind. Green tea and coffee were the most potent staining agents, while Pepsi and turmeric induced lesser color changes.

Because it is related with religious education, the madrassah education system has a distinct place in Pakistani society. This educational system works concurrently with the other educational systems. Madrassahs have their origins in the early days of Islam, when mosques were used as learning institutions for the Muslim community. Following the 9/11 attacks, Pakistan's madrassahs came under fire. The international society has chastised madrassahs for their engagement in militant activities, which, according to popular belief, is the primary cause of terrorism. Following 9/11, Pakistan began reforming madrassahs and revising their curricula.