Having two children might be best for women's mental health: Evidence from UK Biobank.

BACKGROUND: Limited research has examined the association between the number of live births and bipolar disorder and major depression (BDMD). This study aims to investigate the relationship between the number of live births and BDMD among women in the UK, providing a theoretical basis for reproductive decision-making. METHODS: This cross-sectional study utilized data from >55,000 women in the UK Biobank. Inverse probability treatment weighting (IPTW) logistic regression models were employed to reduce bias and confounding. Multivariate logistic regression was applied to examine the relationship between live births and BDMD, while restricted cubic spline regression was utilized to analyze the nonlinear association between the number of live births and BDMD. Subgroup analyses were conducted, stratifying by age, BMI, spontaneous abortion, induced abortion, and spontaneous abortion. RESULTS: The IPTW-multivariate logistic regression analysis indicated that live births serve as an independent protective factor against BDMD. In IPTW-multivariate Model 4, the odds ratio (OR) for the live births group was 0.71 (95 % CI 0.69-0.73, P < 0.001). A significant nonlinear relationship was identified between the number of live births and the risk of BDMD. Additionally, it was found that women with two live births exhibited the lowest risk of BDMD. CONCLUSION: Our study demonstrated that live births are an independent protective factor against BDMD. This research holds particular significance in the context of globally declining fertility rates and the increasing prevalence of mental disorders. It provides guidance for women on fertility decisions amid declining global fertility rates and increasing mental health issues.

Optimal features selection in the high dimensional data based on robust technique: Application to different health database.

Bio-informatics and gene expression analysis face major hurdles when dealing with high-dimensional data, where the number of variables or genes much outweighs the number of samples. These difficulties are exacerbated, particularly in microarray data processing, by redundant genes that do not significantly contribute to the response variable. To address this issue, gene selection emerges as a feasible method for identifying the most important genes, hence reducing the generalization error of classification algorithms. This paper introduces a new hybrid approach for gene selection by combining the Signal-to-Noise Ratio (SNR) score with the robust Mood median test. The Mood median test is beneficial for reducing the impact of outliers in non-normal or skewed data since it may successfully identify genes with significant changes across groups. The SNR score measures the significance of a gene's classification by comparing the gap between class means and within-class variability. By integrating both of these approaches, the suggested approach aims to find genes that are significant for classification tasks. The major objective of this study is to evaluate the effectiveness of this combination approach in choosing the optimal genes. A significant P-value is consistently identified for each gene using the Mood median test and the SNR score. By dividing the SNR value of each gene by its significant P-value, the Md score is calculated. Genes with a high signal-to-noise ratio (SNR) have been considered favorable due to their minimal noise influence and significant classification importance. To verify the effectiveness of the selected genes, the study utilizes two dependable classification techniques: Random Forest and K-Nearest Neighbors (KNN). These algorithms were chosen due to their track record of successfully completing categorization-related tasks. The performance of the selected genes is evaluated using two metrics: error reduction and classification accuracy. These metrics offer an in-depth assessment of how well the selected genes improve classification accuracy and consistency. According to the findings, the hybrid approach put out here outperforms conventional gene selection methods in high-dimensional datasets and has lower classification error rates. There are considerable improvements in classification accuracy and error reduction when specific genes are exposed to the Random Forest and KNN classifiers. The outcomes demonstrate how this hybrid technique might be a helpful tool to improve gene selection processes in bioinformatics.

Pashto script and graphics detection in camera captured Pashto document images using deep learning model.

Layout analysis is the main component of a typical Document Image Analysis (DIA) system and plays an important role in pre-processing. However, regarding the Pashto language, the document images have not been explored so far. This research, for the first time, examines Pashto text along with graphics and proposes a deep learning-based classifier that can detect Pashto text and graphics per document. Another notable contribution of this research is the creation of a real dataset, which contains more than 1,000 images of the Pashto documents captured by a camera. For this dataset, we applied the convolution neural network (CNN) following a deep learning technique. Our intended method is based on the development of the advanced and classical variant of Faster R-CNN called Single-Shot Detector (SSD). The evaluation was performed by examining the 300 images from the test set. Through this way, we achieved a mean average precision (mAP) of 84.90%.

Optimization of Distributed Energy Resources Operation in Green Buildings Environment.

Without a well-defined energy management plan, achieving meaningful improvements in human lifestyle becomes challenging. Adequate energy resources are essential for development, but they are both limited and costly. In the literature, several solutions have been proposed for energy management but they either minimize energy consumption or improve the occupant's comfort index. The energy management problem is a multi-objective problem where the user wants to reduce energy consumption while keeping the occupant's comfort index intact. To address the multi-objective problem this paper proposed an energy control system for a green environment called PMC (Power Management and Control). The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The combination of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is performed to make a fusion methodology to improve the occupant comfort index (OCI) and decrease energy utilization. The proposed framework gives a better OCI when compared with its counterparts, the Ant Bee Colony Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the PMC gives practically the same OCI but consumes less energy. The PMC framework additionally accomplished the ideal OCI (i-e 1) when compared with the existing model, FA-GA (i-e 0.98). The PMC model consumed less energy as compared to existing models such as the ABCKB, GAP, PSO, and AEO. The PMC model consumed a little bit more energy than the SOHP but provided a better OCI. The comparative outcomes show the capability of the PMC framework to reduce energy utilization and improve the OCI. Unlike other existing methodologies except for the AEO framework, the PMC technique is additionally confirmed through a simulation by controlling the indoor environment using actuators, such as fan, light, AC, and boiler.

Deep learning-based recognition system for pashto handwritten text: benchmark on PHTI.

This article introduces a recognition system for handwritten text in the Pashto language, representing the first attempt to establish a baseline system using the Pashto Handwritten Text Imagebase (PHTI) dataset. Initially, the PHTI dataset underwent pre-processed to eliminate unwanted characters, subsequently, the dataset was divided into training 70%, validation 15%, and test sets 15%. The proposed recognition system is based on multi-dimensional long short-term memory (MD-LSTM) networks. A comprehensive empirical analysis was conducted to determine the optimal parameters for the proposed MD-LSTM architecture; Counter experiments were used to evaluate the performance of the proposed system comparing with the state-of-the-art models on the PHTI dataset. The novelty of our proposed model, compared to other state of the art models, lies in its hidden layer size (i.e., 10, 20, 80) and its Tanh layer size (i.e., 20, 40). The system achieves a Character Error Rate (CER) of 20.77% as a baseline on the test set. The top 20 confusions are reported to check the performance and limitations of the proposed model. The results highlight complications and future perspective of the Pashto language towards the digital transition.

Touch or click friendly: Towards adaptive user interfaces for complex applications.

This study evaluated the usability of a direct manipulation device (touchscreen) vs. indirect manipulation devices (mouse and touchpad) on the selected Microsoft (MS) Word tasks as per ISO-9241-11 standard. MS Word was taken as an example of a complex application. The tasks were evaluated in terms of touch-friendly or click-friendly using efficiency, effectiveness, and satisfaction parameters to propose a customized task menu. The experiment was conducted with fifty-four participants, divided into three MS Word usage-based expertise groups. Each participant performed fifty-six tasks using a mouse, a touchpad, and a touchscreen. To assess task-level usability, individual one-way ANOVAs were performed for each task to gauge both efficiency and effectiveness. It's worth noting that the touchscreen significantly outperformed other input methods in just one specific task regarding effectiveness. Consequently, an ANCOVA was employed, with task completion time as the independent variable and the number of errors as a covariate, to further investigate effectiveness. A total of 19 (34%) of the total tasks were found to be significantly efficient with a mouse, while 21 (37.5%) were significantly efficient with a touchscreen. Based on the results, a customized menu is recommended for MS Word-like applications that combine actions in touch-friendly tasks and mouse-friendly tasks separately.

Microplastic Quantification in Aquatic Birds: Biomonitoring the Environmental Health of the Panjkora River Freshwater Ecosystem in Pakistan.

Microplastic pollution has become a global concern, with potential negative impacts on various ecosystems and wildlife species. Among these species, ducks (Anas platyrhynchos) are particularly vulnerable due to their feeding habits and proximity to aquatic environments contaminated with microplastics. The current study was designed to monitor microplastic (MP) pollutants in the freshwater ecosystem of the Panjkora River, Lower Dir, Pakistan. A total of twenty (20) duck samples were brought up for four months and 13 days on the banks of the river, with no food intake outside the river. When they reached an average weight of 2.41 ± 0.53 kg, all samples were sacrificed, dissected, and transported in an ice box to the laboratory for further analysis. After sample preparation, such as digestion with 10% potassium hydroxide (KOH), density separation, filtration, and identification, the MP content was counted. A total of 2033 MP particles were recovered from 20 ducks with a mean value of 44.6 ± 15.8 MPs/crop and 57.05 ± 18.7 MPs/gizzard. MPs detected in surface water were 31.2 ± 15.5 MPs/L. The major shape types of MPs recovered were fragments in crop (67%) and gizzard (58%) samples and fibers in surface water (56%). Other types of particles recovered were fibers, sheets, and foams. The majority of these detected MP particles were in the size range of 300-500 µm (63%) in crops, and 50-150 µm (55%) in gizzards, while in water samples the most detected particles were in the range of 150-300 µm (61%). Chemical characterization by FTIR found six types of polymers. Low-density polyethylene (LDPE) had the greatest polymer detection rate (39.2%), followed by polyvinyl chloride (PVC) (28.3%), high-density polyethylene (HDPE) (22.7%), polystyrene (6.6%), co-polymerized polypropylene (2.5%), and polypropylene homopolymer (0.7%). This study investigated the presence of microplastics in the crops and gizzards of ducks, as well as in river surface water. The results revealed the significant and pervasive occurrence of microplastics in both the avian digestive systems and the surrounding water environment. These findings highlight the potential threat of microplastic pollution to wildlife and ecosystems, emphasizing the need for further research and effective mitigation strategies to address this pressing environmental concern.

Designing a novel chimeric multi-epitope vaccine subunit against Staphylococcus argenteus through artificial intelligence approach integrating pan-genome analysis, in vitro identification, and immunogenicity profiling.

Staphylococcus argenteus is a newly identified pathogen that causes respiratory tract infections, skin infections, such as cellulitis, abscesses, and impetigo, and currently, there is no licensed vaccine available against it. To develop a vaccine against S. argenteus, a bacterial pan-genome analysis was applied to identify potential vaccine candidates. A total of 4908 core proteins were retrieved and utilized for identifying four proteins, including SG38 Panton-Valentine leukocidin LukS-PV protein, SG62 staphylococcal enterotoxin type A protein, SG39 enterotoxin B protein, and SG43 enterotoxin type C3 protein as potential vaccine candidates. Epitopes were predicted for these proteins using different types of B and T-cell epitope prediction tools, and only those with a non-toxic profile, antigenic, non-allergenic, and immunogenic were selected. The selected epitopes were linked to each other to form a multi-epitope vaccine construct, which was further linked to the PADRE sequence (AKFVAAWTLKAAA) and 50s ribosomal L7/L12 protein to enhance the vaccine's antigenicity. The three-dimensional structure of the vaccine construct was assessed to determine its binding affinity with key Toll-like receptor 9 (TLR-9) and Toll-like receptor 5 (TLR-5) immune cell receptors. Our findings demonstrate that the vaccine exhibits favorable binding interactions with these immune cell receptors, indicating its potential efficacy. Molecular dynamic simulations further confirmed the accessibility of vaccine epitopes to the host immune system, substantiating its ability to elicit protective immune responses. Taken together, this study highlights the promising candidacy of the modeled vaccine construct for future in vivo and in vitro experimental investigations.Communicated by Ramaswamy H. Sarma.

Prevalence of type 2 diabetes and pre-diabetes in Sri Lanka: a systematic review and meta-analysis.

OBJECTIVE: The purpose of this research was to determine the prevalence of diabetes and pre-diabetes in Sri Lanka. DESIGN: Systematic review and meta-analysis. DATA SOURCES: MEDLINE (via PubMed), Web of Science, Sri Lankan Journals online and Google Scholar were searched for relevant articles published between January 1990 and June 2022 investigating the prevalence of pre-diabetes and diabetes in Sri Lanka. METHODS: Random effect meta-analyses were conducted to derive the pooled prevalence of pre-diabetes and diabetes and their 95% CIs. Heterogeneity was explored by subgroup and meta-regression analyses. Sensitivity analyses were used to evaluate the impact of any single study on the pooled estimates. Two authors screened articles, extracted data and evaluated the quality of selected studies. RESULTS: A total of 479 articles were reviewed, and 15 studies (n=30 137 participants) were selected in the final analysis. The overall pooled prevalence of diabetes was 12.07% (95% CI, 8.71% to 15.89%; prediction interval: 1.28-31.35). The pooled pre-diabetes prevalence was 15.57% (95% CI, 9.45% to 22.88%; prediction interval: 0.02-49.87). The pooled type 2 diabetes prevalence was the highest in the latest period of 2011-2021 (17.25%) than in the period of 2000s (11.84%) and 1990s (5.62%). CONCLUSIONS: The growing trend of diabetes and pre-diabetes over the last 30 years is alarming in Sri Lanka. The government of Sri Lanka needs to take steps to improve diabetes education, screening, diagnosis and treatment. PROSPERO REGISTRATION NUMBER: CRD42021288591.

MIR149 rs2292832 and MIR499 rs3746444 Genetic Variants Associated with the Risk of Rheumatoid Arthritis.

INTRODUCTION: MicroRNAs (miRNAs) are small non-coding RNAs that play a key role in post-transcriptional modulation of individual genes' expression. Several miRNA variants from different populations are known to be associated with an increased risk of rheumatoid arthritis (RA). AIM: This study was undertaken with the aim to investigate the association of single nucleotide variants; namely, rs2292832, rs3746444, rs11614913, rs1044165, and rs767649 of MIR149, MIR499, MIR196, MIR223, and MIR155, respectively, with RA in the Pakistani population. METHODS: A case-control study was performed by recruiting and genotyping a total of 600 individuals (300 cases and 300 controls) for these five variants using a TaqMan single-nucleotide polymorphism (SNP) genotyping assay. The resultant genotypic data was statistically analyzed through a chi-squared test for its association with RA under different inheritance models. RESULTS: We found a significant association of rs2292832 with RA at genotypic (co-dominant (p < 0.0001), dominant (CC vs. TT + CT: OR 2.063 (1.437-2.962); p = 0.0001), recessive (TT vs. CT + CC: OR 0.376 (0.259-0.548); p < 0.0001)), and allelic (allele C) levels ((OR 0.506 (0.402-0637); p < 0.0001)). Similarly, the rs3746444 showed a significant association with RA under co-dominant (p = 0.0001), dominant (GG vs. AA + AG: OR 5.246 (3.414-8.061); p < 0.0001), recessive (AA vs. GG + AG: OR 0.653 (0.466-0.916); p = 0.014), and additive models (G vs. A; OR 0.779 (0.620-0.978); p = 0.03). However, we did not observe any significant association of rs11614913, rs1044165, or rs767649 with RA in our subjects. CONCLUSION: To our knowledge, this was the first study that investigated and found an association between functional polymorphisms in miRNAs and RA in the Pakistani population.

An analysis of the impact of China's macroeconomic performance on its trade partners: Evidence based on the GVAR model.

Economic strategies and planning are critical to a country's growth and development. China, like many other countries, is seeking the most cost-effective trade deals. Using the Global Vector Auto Regression (GVAR) model, this study examined the impact of a shock to China's macroeconomic factors on trading economies. The major findings reveal that there is no co-movement between the shock in Chinese gross domestic product (GDP) and German macroeconomic indicators; however, the shock has a positive and substantial influence on Japan's GDP and Unites States (US)' exchange rate. It is also worth noting that a shock to Chinese trade volume is more susceptible and more disturbing than a shock to US trade volume since it reduces trade volume and causes the Ren Min Bi (RMB) to devalue permanently. Furthermore, the analysis shows that Chinese stock prices have a major influence on German economy since China's GDP, trade volume, and currency appreciate over time when its stock price rises. Finally, the exchange rate shock is beneficial to Germany as it boosts GDP and trade volume but has a negative influence on US stock prices. The current study is, therefore, expected to be a suitable beginning point for the governments and policymakers of trading partners to design an effective trade policy to minimize the impact on major economic variables.

An analysis of the success of fiscal adjustment in reducing public debt: Evidence from Pakistan.

This study seeks answers to questions such as: what is fiscal adjustment? Which fiscal strategy will result in a reduction in public debt liabilities? In the pursuit of answers to these questions, the study has defined two objectives. Firstly, the fiscal adjustment episodes must be identified in order to detach the discretionary fiscal stance; secondly, the success of these adjustment episodes in reducing public debt liabilities must be assessed. As a result, attempts will be made to undertake analyses that would simplify the issues underlying Pakistan's practical policy options. A total of eleven adjustment episodes have been observed in a sample, ranging from 1976 to 2017, following Alesina and Ardagna's definition. The descriptive analysis reveals that five episodes succeeded in reducing the public debt, while six episodes failed to reduce the ratio. Out of the five successful episodes, four are found to be spending-based and one is tax-based. To quantify the success of fiscal adjustment, the empirical model has been calibrated on Leibrecht and Scharler's model and estimation is done via both the Ordinary Least Squares (OLS) and Robust Least Squares (RLS) methods. The RLS method produces better outcomes than the OLS method. Under RLS, all variables are significant except GDP growth, whereas in the OLS model, the election year and regime shift, together with GDP growth, are statistically insignificant. The fiscal adjustment's composition reveals that spending-based consolidation boosts the chances of the fiscal adjustment's success. Fiscal authorities should, therefore, adopt spending-based austerity measures to ensure the sustainability of public finances and prevent the negative macroeconomic consequences of unsustainable public debt.

Improvement in growth and yield attributes of cluster bean through optimization of sowing time and plant spacing under climate change scenario.

Cluster bean (Cyamopsis tetragonoloba L.) yield has plateaued due to reduction in rainfall and rise in temperature. Therefore, its production cycle could not get appropriate water and temperature. It becomes important to standardize the sowing time and plant spacing of cluster beans in changing climate scenarios to get higher productivity. Therefore, a field study was conducted in 2019 at the Research area of MNS-University of Agriculture, Multan, Pakistan to evaluate the effect of four sowing times (15th May, 1st June, 15th June, and 1st July) and three plant spacings (10, 12 and 15 cm) on crop growth, yield, and physiological functions of cluster bean genotype BR-2017 under split plot arrangement under randomized complete block design (RCBD) with three replications. The sowing times (15th May, 1st June, 15th June, and 1st July) were placed in the main plot, while plant spacing (10, 12 and 15 cm) was maintained in subplots. The significant effect of sowing time and plant spacing was observed on pod plant-1, pod length, grain yield, and 1000-grain weight. Results showed that 1st June sowing performed better over 15th May, 15th June, and 1st July, while plant spacing 15 cm about in all sowing times showed higher results on growth and yield parameters of cluster bean over plant spacing 10, 12, and 15 cm. The 1st June sowing time at 15 cm plant spacing showed 8.0, 22.7, and 28.5% higher grains pod-1 than 15th May, 15th June, and 1st July sowing, respectively. Maximum grain yield was observed on 1st June in all three spacings (10, 12, and 15 cm). The chord diagram indicates that the crop has received optimum environmental conditions when sown 1st June over other sowing times. In conclusion, 1st June sowing with 15 cm plant spacing could be a good option to achieve maximum productivity of cluster bean under changing climate scenario.

Bowman's layer transplantation in advanced keratoconus; 18-months outcomes.

PURPOSE: To assess the outcome of isolated Bowman's layer transplantation (BLT) in advanced keratoconus in the patients not suitable for ultra violet cross-linking and intra stromal corneal ring segments. STUDY DESIGN: Nonrandomized Quasi-Experimental. MATERIALS AND METHODS: Mid corneal stromal dissection followed by implantation of manually isolated bowman layer from a donor into the recipient stromal pocket was performed in the Department of Ophthalmology Khyber Teaching Hospital Peshawar from September 2018 to October 2018 and followed up over 18 months. RESULTS: Eleven eyes of 11 patients, 8 male and 3 female within the age range of 7-28 years with progressive keratoconus went under BLT All keratometry values decreased after surgery. Mean anterior pre-operative SimK changed from 69.05 ± 10.85 D to 61.14 ± 6.23 D at 18 months post-operatively (P = 0.005). Means pre-operative Kmax value decreased from 77.24 ± 7.58 D to 71.07 ± 5.37 D at 18 months post-operatively (p = 0.060). The mean pre-operative posterior keratometry value changed from - 10.03 ± 0.88 to - 8.96 ± 1.06 D at 18 months post-operatively (P = 0.002). Mean pre-operative thinnest corneal thickness increased from 281.64 ± 196.86 um to 355.27 ± 19.17 um at 18th months post-operatively (P = 0.001). Pachymetry p-value pre-operative to 18 months (P = 0.001) was statistically significant. Mean anterior SimK at 6 months post-operatively, 60.55 ± 5.56D changed to 61.14 ± 6.23D at 18 months post-operatively (P = 0.000), Kmax at 6 months postoperative 71.60 ± 5.01 D changed to 71.07 ± 5.37 D at 18 months post-operatively (P = 0.008). Both anterior K values at 6 months and 18 months post-operatively showed less significant change, and the same was true for Kmax data at 6 months and 18 months post-operatively. P-values at 6 months to 18 months post-operatively for cornea back data remained (P = 0.001) unchanged. No complications were observed intra-operatively or post-operatively. CONCLUSION: Bowman layer transplantation is a unique surgical treatment for advanced keratoconus to stabilize progressive ectasia with fewer complications avoiding the need for penetrating or deep anterior lamellar keratoplasty.

Risk Factors And Treatment Outcome Of Children With HCV Infection.

BACKGROUND: Combination of pegylated interferon α-2a or α-2b has been considered to be the standard for treating HCV infection among children. Many new agents inhibiting HCV during various steps while replicating is under study around the world. This study was aimed to note the efficacy of sofosbuvir and ribavirin among children having HCV infection. METHODS: This was an open label experimental trail done at Department of Gastroenterology, Children Hospital and The Institute of Child Health, Multan. The study duration was from July to December 2019. A total of 89 HCV treatment naïve children aged 6-16 years of age, having HCV PCR as positive were enrolled. Sofosbuvir as 400 mg once a day along with ribavirin 10-15 mg per kg per day in the form of once or twice as divided doses were given in all the cases. After starting the treatment, along with side effects, rapid virological response (RVR) as PCR at 4 weeks, early virological response (EVR) at 12 weeks and post treatment 12 weeks HCV PCR as sustained virological response (SVR) was noted. RESULTS: Out of a total of 89 children, there were 53 (59.6%) boys and 36 (40.4%) girls. Mean age was noted to be 12.42±2.57 years. Majority of the children, 72 (80.9%) had genotype 3 while genotype 1 was noted in 11 (12.4%) and un-typable in remaining 6 (6.7%). History of blood or blood products transfusion was seen to be the commonest mode of HCV transmission, found in 41 (46.1%) children, perinatal transmission in 20 (22.5%) and history of previous surgery in 9 (10.1%). Rapid virological response was noted in 73 (83.1%) children, all 89 (100%) children achieved EVR whereas SVR was noted in 86 (96.7%). Headache was the commonest side effect, reported by 24 (27.0%) followed by nausea in 15 (16.9%). CONCLUSIONS: Regardless of the genotype, sofosbuvir and ribavirin combination therapy was noted to have excellent efficacy amongst children with HCV infection. History of blood and blood product transfusion was the commonest risk factor found.

Spammer group detection and diversification of customers' reviews.

Online reviews regarding different products or services have become the main source to determine public opinions. Consequently, manufacturers and sellers are extremely concerned with customer reviews as these have a direct impact on their businesses. Unfortunately, to gain profit or fame, spam reviews are written to promote or demote targeted products or services. This practice is known as review spamming. In recent years, Spam Review Detection problem (SRD) has gained much attention from researchers, but still there is a need to identify review spammers who often work collaboratively to promote or demote targeted products. It can severely harm the review system. This work presents the Spammer Group Detection (SGD) method which identifies suspicious spammer groups based on the similarity of all reviewer's activities considering their review time and review ratings. After removing these identified spammer groups and spam reviews, the resulting non-spam reviews are displayed using diversification technique. For the diversification, this study proposed Diversified Set of Reviews (DSR) method which selects diversified set of top-k reviews having positive, negative, and neutral reviews/feedback covering all possible product features. Experimental evaluations are conducted on Roman Urdu and English real-world review datasets. The results show that the proposed methods outperformed the existing approaches when compared in terms of accuracy.

Etiologies and outcome of lower gastrointestinal bleeding in patients presenting to a tertiary care Children's Hospital.

OBJECTIVE: To determine etiology and outcome of children with lower gastrointestinal bleeding (LGIB). METHODS: This was a prospective study conducted at the Department of Pediatric Gastroenterology and Hepatology, Children's Hospital and The Institute of Child's Heath, Multan, Pakistan, from July 2019 to March 2020. A total of 148 cases presented with bleeding per rectum and underwent colonoscopy, were included. Children of both genders and aged three month to 15 years were included. Detailed history, clinical examination, laboratory studies, colonoscopy and histopathology were done in all cases. Study information like demographics, complaints, general clinical examination, colonoscopy and histopathological findings were recorded. RESULTS: Overall, mean age was noted to be 7.20±1.83 years. Abdominal pain was reported in 41 (27.7%), diarrhea 36 (24.3%), fever 12 (8.1%) and constipation in 4 (2.7%). Pallor was noted among 68 (45.9%), weight loss 39 (26.3%) and tachycardia 31 (20.9%). Colonoscopy revealed juvenile colonic / rectal polyps, infectious colitis and solitary rectal ulcer (SRU) as the most common etiologies found among 58 (39.2%), 20 (13.5%) and 19 (12.8%) cases respectively. Juvenile polyps and non-specific colitis were the commonest histopathological findings seen in 55 (37.2%) and 20 (13.5%) cases respectively. Colonoscopic polypectomy was used to remove all juvenile polyps. CONCLUSION: LGIB is presentation of various underlying causes. Children with LGIB commonly present with abdominal pain. Juvenile polyps were the most frequent cause of LGIB among children flowed by non-specific colitis. Most of the children having LGIB were diagnosed and treated successfully, few are in remission and very few were found resistant to treatment.

Ocular Surface Squamous Neoplasia: Clinicopathological Analysis and Treatment Outcome of 65 Cases.

OBJECTIVES: To find out the demography, clinical behaviour and outcome of ocular surface squamous neoplasia (OSSN) in North-west Pakistan. STUDY DESIGN: Case series. PLACE AND DURATION OF STUDY: Department of Ophthalmology, Khyber Teaching Hospital, Peshawar, Pakistan, from March 2011 to December 2017. METHODOLOGY: All patients with OSSN consulting this department were registered. A special proforma was designed to document demography, clinical features, management, and follow-up of all cases. At the end of study, the data was analysed using SPSS version 25 and the result compiled. RESULTS: Sixty-five eyes with OSSN were included in the study, in which 40 (61.5%) were males and 25 (38.5%) were females. Mean age of the patients was 61.68 +12.76 years. Twenty-six (40%) patients were farmers and exposed to ultraviolet light for prolong period. Fifty-nine (90.8%) presented with a painless, reddish mass on ocular surface. Average size of neoplasm at presentation was 18.60 + 11.30 mm in large dimension. Histopathologically, 23 (35.4%) were carcinoma in situ and 41(63%) invasive squamous cell carcinoma. During the period of follow-up 41 (63.1%) cured, 21 (32.2%) developed recurrence, and 3 (4.6%) patients passed away. CONCLUSION: OSSN is common in North-west Pakistan. The presentation is late due to certain socio-economic factors; but overall behaviour of the neoplasm is almost the same as mentioned in other studies from different parts of the world. Key Words: Ocular surface squamous neoplasia (OSSN), conjunctival squamous cell carcinoma, Carcinoma in situ.

Investigation of P T- and P T-antisymmetry in two dimensional (2D) optical lattices.

A collection of cold rubidium atoms in three-level configuration trapped in two dimensional (2D) optical lattices is revisited. The trapped atoms are considered in the Gaussian density distribution and we study the realization of P T-, non-P T-, and P T-antisymmetry in 2D optical lattices. Such a fascinating modulation is achieved by spatially modulating the intensity of the driving field. Interestingly, control over P T- to non-P T-symmetry and vice versa in 2D optical lattices is achieved via a single knob such as microwave field, probe field and relative phase of optical and microwave fields. In addition, control over P T-antisymmetry to non-P T-symmetry and vice versa is also achieved via relative phase. The coherent control of P T- non-P T- and P T-antisymmetry in optical susceptibility of 2D atomic lattices can be extended to 2D optical devices including modulators, detectors, and the 2D atomic lattices can also be extended to photonic transistors and diodes.

Synergistic effect of a novel chitosan/silica nanocomposites-based formulation against gray mold of table grapes and its possible mode of action.

Ecofriendly nanostructured materials have been proposed as promising alternative control means to prevent plant diseases. Chitosan nanoparticles (CN), silica nanoparticles (SN) and chitosan-silica nanocomposites (CSN) were synthesized and their morphology and structure was characterized by transmission electron microscope (TEM), scanning electron microscopy (SEM), infrared spectra (FT-IR) and Raman spectroscopy. Their antifungal efficiency against Botrytis cinerea, the causal fungus of gray mold disease of table grapes, was tested in vitro and in vivo (under artificial and natural infections). In vitro tests showed that CN, SN and CSN reduced fungal growth by 72, 76 and 100%, respectively at 1% as compared to control. Under natural infection, at the end of cold storage, CSN was the most effective treatment, and reduced the development of gray mold by 59 and 83%, for 'Italia' and 'Benitaka' grapes, respectively as compared to the water control. Results indicate that a synergistic effect of CSN against gray mold was observed. The impact of tested nanocomposites on soluble solids - TSS, titratable acidity - TA, TSS/TA, berry color, mass loss, stem browning and shattered berries was investigated. No negative effect of tested nanomaterials in term of grape quality was observed. For 'Italia' table grape, CN and CSN can preserve bunches from mass loss as compared to control. Also, the effect of CSN on reactive species of oxygen (ROS), ATP content and mitochondrial membrane potential (MMP) of B. cinerea spores was determined to verify its mode of action. The obtained results suggested CSN, as alternative control means, to reduce/substitute the use of fungicides to control gray mold of table grapes while maintaining grape quality.