Impact of dental caries on the daily lives of geriatric patients visiting dental hospitals in Rawalpindi, Pakistan.

Background: The objectives were to assess the impact of dental caries on the daily living of the geriatric population and determine the factors that influence the relationship between dental health and the daily living of the geriatric population. Methods: A descriptive cross-sectional study was carried out over six months at Rawalpindi's public and private dental hospitals. Participants aged≥60 years, both male and female, were selected. The calculated sample size was 281. Desired sample from one of the dental hospitals was collected using a non-probability consecutive sampling strategy. Data about sociodemographic characteristics and the DMFT index were collected. Adapted validated tool dental impact on daily living (DIDL) was used to assess the impact of dental health on daily living. Results: Chi-squared test of association showed a positive association between the DIDL and sociodemographic variables, including age (P=0.001), gender (P=0.001), education (P=0.001), income (P=0.001), occupation (P=0.029), marital status (P=0.001), living arrangement (P=0.001), and history of chronic illnesses (P=0.001). The association between the DMFT index and DIDL also showed statistically significant results (P=0.001). Binary logistic regression analysis indicated that gender (OR=6.98, P=0.005) and the individual's dental health (OR=6.43, P=0.001) were the strongest predictors of the impact experienced in daily life activities. The overall model was statistically significant (χ2=51.24, P=0.001), and the variables were responsible for 32.4% of the variance in the outcome variable. Conclusion: The study provides strong evidence that sociodemographic factors, DMFT index, gender, and individual dental health significantly contribute to the impact of dental health on daily living. Gender and individual dental health emerge as particularly influential predictors. These findings emphasize the need for targeted interventions and awareness programs, especially for groups with a higher risk of experiencing a significant impact on daily life due to dental issues.

Improvement of the Self-Controlled Hyperthermia Applications by Varying Gadolinium Doping in Lanthanum Strontium Manganite Nanoparticles.

In this study, silica-encapsulated gadolinium was doped in lanthanum strontium manganite nanoparticles (NPs) with different concentrations using the citrate-gel auto-combustion method. We focused on tuning the Curie temperature and enhancing the specific absorption rate (SAR) of silica-coated gadolinium-doped lanthanum strontium manganite NPs to make them suitable for self-controlled magnetic hyperthermia. The samples were characterized by using transmission electron microscopy (TEM), X-ray diffraction, Fourier transform infrared spectroscopy (FTIR), and magnetic measurements to examine the structural, optical, and magnetic properties of the manganite NPs. While our results exhibit a successful doping of gadolinium in lanthanum strontium manganite NPs, we further prepared magnetic core NPs with sizes between 20 and 50 nm. The Curie temperature of the NPs declined with increasing gadolinium doping, making them promising materials for hyperthermia applications. The Curie temperature was measured using the magnetization (M-T) curve. Magnetic heating was carried out in an external applied AC magnetic field. Our present work proved the availability of regulating the Curie temperature of gadolinium-doped lanthanum strontium manganite NPs, which makes them promising candidates for self-controlled magnetic hyperthermia applications.

Assessment of the Dimensions of Coronary Arteries for the Manifestation of Coronary Artery Disease.

INTRODUCTION: The size of the coronary artery influences the effective outcome of therapeutic measures like coronary artery bypass graft (CABG) surgery, percutaneous coronary interventions (PCI), and diagnosis of coronary artery disease. Patients' age, gender, BMI, anatomical variations, and increased left ventricular size all have an effect on coronary artery parameters. OBJECTIVE: This study aims to compare the average size of the coronary arteries of the Pakistani population in both sexes for manifestation of coronary artery disease. METHODOLOGY: For the analysis of the coronary arteries, 100 patients of both sexes, male and female, were taken. X-ray angiography was performed for two-dimensional images of coronary arteries. For diameter measurement, images were visualized on quantitative coronary angiography (QCA) in different views (caudal and cranial views). The diameters of the left main coronary artery (left main stem/LMS), left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA) were measured on angiograms. Data about the dimensions of the coronary artery was gathered through quantitative angiography. Data analysis was done through SPSS version 26 (IBM Corp., Armonk, NY). RESULTS:  There is a notable distinction in the average diameters among the proximal LAD (3.12), mid-LAD (2.40), and distal LAD (1.29). A statistically significant difference is evident among mid-LCx, distal LCx, and proximal LCx (p-value < 0.001). Likewise, the average diameter of the distal RCA (1.89) was smaller when compared to the mid-RCA (3.19) and proximal RCA (3.78). However, there was no significant difference in the average diameter among mid-LMS, distal LMS, and proximal LMS (p-value = 0.09). CONCLUSION: The average diameter of distal RCA was smaller when compared to mid-RCA and proximal RCA. The average size of proximal LAD and proximal LCx was comparatively larger than mid- and distal LAD and LCx. The findings of current research will be beneficial for the diagnosis and management of coronary artery disease patients.

Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review.

The core idea behind precision medicine is to pinpoint the subpopulations that differ from one another in terms of disease risk, drug responsiveness, and treatment outcomes due to differences in biology and other traits. Biomarkers are found through genomic sequencing. Multi-dimensional clinical and biological data are created using these biomarkers. Better analytic methods are needed for these multidimensional data, which can be accomplished by using artificial intelligence (AI). An updated review of 80 latest original publications is presented on four main fronts-preventive medicine, medication development, treatment outcomes, and diagnostic medicine-All these studies effectively illustrated the significance of AI in precision medicine. Artificial intelligence (AI) has revolutionized precision medicine by swiftly analyzing vast amounts of data to provide tailored treatments and predictive diagnostics. Through machine learning algorithms and high-resolution imaging, AI assists in precise diagnoses and early disease detection. AI's ability to decode complex biological factors aids in identifying novel therapeutic targets, allowing personalized interventions and optimizing treatment outcomes. Furthermore, AI accelerates drug discovery by navigating chemical structures and predicting drug-target interactions, expediting the development of life-saving medications. With its unrivaled capacity to comprehend and interpret data, AI stands as an invaluable tool in the pursuit of enhanced patient care and improved health outcomes. It's evident that AI can open a new horizon for precision medicine by translating complex data into actionable information. To get better results in this regard and to fully exploit the great potential of AI, further research is required on this pressing subject.

Short-term solar energy forecasting: Integrated computational intelligence of LSTMs and GRU.

Problems with erroneous forecasts of electricity production from solar farms create serious operational, technological, and financial challenges to both Solar farm owners and electricity companies. Accurate prediction results are necessary for efficient spinning reserve planning as well as regulating inertia and power supply during contingency events. In this work, the impact of several climatic conditions on solar electricity generation in Amherst. Furthermore, three machine learning models using Lasso Regression, ridge Regression, ElasticNet regression, and Support Vector Regression, as well as deep learning models for time series analysis include long short-term memory, bidirectional LSTM, and gated recurrent unit along with their variants for estimating solar energy generation for every five-minute interval on Amherst weather power station. These models were evaluated using mean absolute error root means square error, mean square error, and mean absolute percentage error. It was observed that horizontal solar irradiance and water saturation deficiency had a highly proportional relationship with Solar PV electricity generation. All proposed machine learning models turned out to perform well in predicting electricity generation from the analyzed solar farm. Bi-LSTM has performed the best among all models with 0.0135, 0.0315, 0.0012, and 0.1205 values of MAE, RMSE, MSE, and MAPE, respectively. Comparison with the existing methods endorses the use of our proposed RNN variants for higher efficiency, accuracy, and robustness. Multistep-ahead solar energy prediction is also carried out by exploiting hybrids of LSTM, Bi-LSTM, and GRU.

Ventilator associated pneumonia in intensive care unit patients: a systematic review.

Ventilator-associated pneumonia (VAP) is the most common ICU acquired pneumonia among patients who are invasively intubated for mechanical ventilation. Patients with VAP suffer an increased mortality risk, financial burden, and length of stay in the hospital. The authors aimed to review the literature to describe the incidence, mortality, and microbiological evidence of VAP. We selected 13 peer-reviewed articles published from 1 January 2010 to 15 September 2022 from electronic databases for studies among adult or pediatric patients diagnosed with VAP expressed per thousand days admitted in the ICU. The VAP rates ranged from 7 to 43 per thousand days, varying among different countries of the world. A significant rate of mortality was observed in 13 studies ranging from 6.3 to 66.9%. Gram-negative organisms like Acinetobacter spp., Pseudomonas aeruginosa Gram-positive organisms like Staphylococcus aureus were frequently found. Our findings suggest an alarming situation of VAP among patients admitted to the intensive care units with increasing incidence and mortality. The review also found that VAP is more common in males and that there is a significant variation in the incidence and mortality rates of VAP among different countries. The findings of this review can inform the development of infection control and prevention strategies to reduce the burden of VAP. Thus, there is a crucial need for control and preventive measures like interventional studies and educational programs on staff training, hand-hygiene, and the appropriate use of ventilator bundle approach to curb this preventable threat that is increasing at an alarming rate.

Epidemiology of premalignant and malignant upper gastrointestinal lesions among patients presenting from a rural community in Karachi, Pakistan.

Objective: Understanding the epidemiology of upper gastrointestinal cancers in Pakistan may help in identifying important demographic risk factors for upper gastrointestinal malignancies in a particular rural population group. This will benefit in implementing tailored prevention approaches as well as effective management of health services. Method: A secondary data analysis of 1193 patients was conducted who went through diagnostic upper GI endoscopy between December 2016 to May 2019 at Fatima Hospital. The endoscopies were performed at Fatima Hospital which is the main health resource for the specifically targeted rural community. Data was analyzed using SPSS version 21. Results: The median age of patients included in the sample was 35 years (IQR=20 years). One third of all endoscopic findings were concluded as normal. The frequency of malignant upper gastrointestinal lesions was relatively higher among male and patients with age 65 years or more. The study didn't find any significant differences in the distribution of malignancies on the basis of ethnicity. Adenocarcinoma of esophagus was the most common malignant lesion. Conclusion: The average age of patients undergoing upper gastrointestinal endoscopy among rural community of Karachi was relatively low. The burden of upper GI malignancies was significantly higher among elderly. Male patients had significantly greater burden of premalignant and malignant lesions as compared to females. No differences in the distribution of diagnostic outcomes were observed on the basis of ethnicity.

DeepBLS: Deep Feature-Based Broad Learning System for Tissue Phenotyping in Colorectal Cancer WSIs.

Tissue phenotyping is a fundamental step in computational pathology for the analysis of tumor micro-environment in whole slide images (WSIs). Automatic tissue phenotyping in whole slide images (WSIs) of colorectal cancer (CRC) assists pathologists in better cancer grading and prognostication. In this paper, we propose a novel algorithm for the identification of distinct tissue components in colon cancer histology images by blending a comprehensive learning system with deep features extraction in the current work. Firstly, we extracted the features from the pre-trained VGG19 network which are then transformed into mapped features space for nodes enhancement generation. Utilizing both mapped features and enhancement nodes, the proposed algorithm classifies seven distinct tissue components including stroma, tumor, complex stroma, necrotic, normal benign, lymphocytes, and smooth muscle. To validate our proposed model, the experiments are performed on two publicly available colorectal cancer histology datasets. We showcase that our approach achieves a remarkable performance boost surpassing existing state-of-the-art methods by (1.3% AvTP, 2% F1) and (7% AvTP, 6% F1) on CRCD-1, and CRCD-2, respectively.

PCGA: Polynomial collocation genetic algorithm for singular Poisson-Boltzmann equation arising in thermal explosions.

Heat generation as a result of the exothermic reaction reaches the environment mainly due to the conduction through the walls of the vessel. The balance between the heat generated and the heat conducted away, resulting in the explosion is described by the Frank-Kamenetzkii (FK) parameter ρ. The critical value of FK for which the explosion occurs depends upon the shape of the vessel, which requires the solution of governing singular nonlinear Poisson-Boltzmann equation. Owing to the exponential nonlinearity and singularity the analytical exact solution for the non-integer k values does not exist. This work focuses on implementing the polynomial collocation by exploiting the global optimization features of the genetic algorithm to solve the Poisson-Boltzmann equation for integer and non-integer shape factors (k). The governing equation was converted into coupled nonlinear algebraic equations and an objective function was formulated. The method was examined for six different configurations of the control parameters of GA to find the best set of parameters. The solution for temperature distribution is obtained for cylindrical (k = 1), parallelepiped (k = 0.438, 0.694), and an arbitrary shape (k = 0.5) respectively. The solution obtained from Polynomial Collocation Genetic Algorithm (PCGA) remained in good agreement with the corresponding analytical results for k = 1, with the minimum absolute error of 10 - 10 . The critical values of the FK are obtained as 1.5 , 1.4 , a n d 1.7 for shape factor k = 0.438 , 0.5 , a n d 0.694 respectively with the convergence of the order of 10 - 6 t o 10 - 5 . The obtained solution is fairly stable over appropriate independent runs with the variation in the fitness value ranging from 10 - 05 t o 10 - 03 . Further simulations were performed to validate the results through statistical error indices. The diminutive errors of the order of 10 - 6 confirm reliable optimum solution, accuracy, and stability.

Efficacy of hypertonic saline versus isotonic saline among children with cystic fibrosis: A systematic review and meta-analysis.

Background: Inhaled hypertonic saline (HS) reduces pulmonary exacerbations in patients with cystic fibrosis (CF) aged 6 or more years. However, the effectiveness of HS in improving clinical outcomes in younger children aged 6 or less years is not established. This study examines the efficacy of HS in younger CF patients. Methods: Searches were conducted across three databases (Medline, Cochrane Central and EMBASE) from inception through July 2022. Randomized controlled trials assessing the impact of HS in younger CF patients were included. Trials involving only patients greater than 6 years or control group other than isotonic saline (IS) were excluded. Outcomes measured included lung clearance index (LCI), cystic fibrosis questionnaire (CFQ-R) score, spirometry measures, oxygen saturation, respiratory rate, height and weight. Outcomes were reported as mean differences (MDs) with 95% confidence intervals. Results: Seven studies (n = 390 patients) were included in this review. HS significantly reduced the LCI (MD: -0.67; 95%CI, -1.05 to 0.29, P = 0.0006) compared to IS. In addition, HS was associated with significant improvements in height (MD: 2.23; 95%CI, -0.00 to 4.46, P = 0.05) and CFQ-R (MD: 4.30; 95%CI, 0.65-7.95, P = 0.02), but not in oxygen saturation (MD: -0.15; 95%CI, -0.54 to 0.25, P = 0.47), respiratory rate (MD: -0.21; 95%CI, -2.19 to 1.77, P = 0.83) or weight (MD: 0.70; 95%CI, -0.47 to 1.87, P = 0.24). Furthermore, HS did not significantly improve spirometry measures, including FEV1 (MD: -0.11; 95%CI, -0.21 to 0.43, P = 0.51) and forced vital capacity (MD: 0.27; 95%CI, -0.49 to 1.04, P = 0.48), but significantly improved FEF25-75 (MD: 0.12; 95% CI, 0.05-0.20; P = 0.002). Discussion: Treatment with HS in younger children with CF improves lung clearance, symptoms and quality of life. FEF25-75 may prove a more sensitive measure for assessing intervention related improvements in pediatric CF trials. Conclusion: The findings support HS as a therapeutic method in CF-affected children.

An attention-based recurrent learning model for short-term travel time prediction.

With the advent of Big Data technology and the Internet of Things, Intelligent Transportation Systems (ITS) have become inevitable for future transportation networks. Travel time prediction (TTP) is an essential part of ITS and plays a pivotal role in congestion avoidance and route planning. The novel data sources such as smartphones and in-vehicle navigation applications allow traffic conditions in smart cities to be analyzed and forecast more reliably than ever. Such a massive amount of geospatial data provides a rich source of information for TTP. Gated Recurrent Unit (GRU) has been successfully applied to traffic prediction problems due to its ability to handle long-term traffic sequences. However, the existing GRU does not consider the relationship between various historical travel time positions in the sequences for traffic prediction. We propose an attention-based GRU model for short-term travel time prediction to cope with this problem enabling GRU to learn the relevant context in historical travel time sequences and update the weights of hidden states accordingly. We evaluated the proposed model using FCD data from Beijing. To demonstrate the generalization of our proposed model, we performed a robustness analysis by adding noise obeying Gaussian distribution. The experimental results on test data indicated that our proposed model performed better than the existing deep learning time-series models in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2).

Vitamin D and rosuvastatin alleviate type-II diabetes-induced cognitive dysfunction by modulating neuroinflammation and canonical/noncanonical Wnt/ß-catenin signaling.

BACKGROUND: Type-II diabetes mellitus (T2DM) is a major risk factor for cognitive impairment. Protecting the brain environment against inflammation, and neurodegeneration, as well as preservation of the BBB veracity through modulating the crosstalk between insulin/AKT/GSK-3ß and Wnt/ß-catenin signaling, might introduce novel therapeutic targets. PURPOSE: This study aimed at exploring the possible neuroprotective potential of vitamin D3 (VitD) and/or rosuvastatin (RSV) in T2DM-induced cognitive deficits. METHODS: T2DM was induced by a high-fat sucrose diet and a single streptozotocin (STZ) dose. Diabetic rats were allocated into a diabetic control and three groups treated with RSV (15 mg/kg/day, PO), VitD (500 IU/kg/day, PO), or their combination. RESULTS: Administration of VitD and/or RSV mitigated T2DM-induced metabolic abnormalities and restored the balance between the anti-inflammatory, IL 27 and the proinflammatory, IL 23 levels in the hippocampus. In addition, they markedly activated both the canonical and noncanonical Wnt/ß-catenin cassettes with stimulation of their downstream molecular targets. VitD and/or RSV upregulated insulin and α7 nicotinic acetylcholine (α7nACh) receptors gene expression, as well as blood-brain barrier integrity markers including Annexin A1, claudin 3, and VE-cadherin. Also, they obliterated hippocampal ApoE-4 content, Tau hyperphosphorylation, and Aß deposition. These biochemical changes were reflected as improved behavioral performance in Morris water maze and novel object recognition tests and restored hippocampal histological profile. CONCLUSION: The current findings have accentuated the neuroprotective potential of VitD and RSV and provide new incentives to expand their use in T2DM-induced cognitive and memory decline. This study also suggests a superior benefit of combining both treatments over either drug alone.

Transfer learning for genotype-phenotype prediction using deep learning models.

BACKGROUND: For some understudied populations, genotype data is minimal for genotype-phenotype prediction. However, we can use the data of some other large populations to learn about the disease-causing SNPs and use that knowledge for the genotype-phenotype prediction of small populations. This manuscript illustrated that transfer learning is applicable for genotype data and genotype-phenotype prediction. RESULTS: Using HAPGEN2 and PhenotypeSimulator, we generated eight phenotypes for 500 cases/500 controls (CEU, large population) and 100 cases/100 controls (YRI, small populations). We considered 5 (4 phenotypes) and 10 (4 phenotypes) different risk SNPs for each phenotype to evaluate the proposed method. The improved accuracy with transfer learning for eight different phenotypes was between 2 and 14.2 percent. The two-tailed p-value between the classification accuracies for all phenotypes without transfer learning and with transfer learning was 0.0306 for five risk SNPs phenotypes and 0.0478 for ten risk SNPs phenotypes. CONCLUSION: The proposed pipeline is used to transfer knowledge for the case/control classification of the small population. In addition, we argue that this method can also be used in the realm of endangered species and personalized medicine. If the large population data is extensive compared to small population data, expect transfer learning results to improve significantly. We show that Transfer learning is capable to create powerful models for genotype-phenotype predictions in large, well-studied populations and fine-tune these models to populations were data is sparse.

LSTM input timestep optimization using simulated annealing for wind power predictions.

Wind energy is one of the renewable energy sources like solar energy, and accurate wind power prediction can help countries deploy wind farms at particular locations yielding more electricity. For any prediction problem, determining the optimal time step (lookback) information is of primary importance, and using information from previous timesteps can improve the prediction scores. This article uses simulated annealing to find an optimal time step for wind power prediction. Finding an optimal timestep is computationally expensive and may require brute-forcing to evaluate the deep learning model at each time. This article uses simulated annealing to find an optimal time step for wind power prediction. The computation time was reduced from 166 hours to 3 hours to find an optimal time step for wind power prediction with a simulated annealing-based approach. We tested the proposed approach on three different wind farms with a training set of 50%, a validation set of 25%, and a test set of 25%, yielding MSE of 0.0059, 0.0074, and 0.010 for each wind farm. The article presents the results in detail, not just the mean square root error.

Can We Convert Genotype Sequences Into Images for Cases/Controls Classification?

Converting genotype sequences into images offers advantages, such as genotype data visualization, classification, and comparison of genotype sequences. This study converted genotype sequences into images, applied two-dimensional convolutional neural networks for case/control classification, and compared the results with the one-dimensional convolutional neural network. Surprisingly, the average accuracy of multiple runs of 2DCNN was 0.86, and that of 1DCNN was 0.89, yielding a difference of 0.03, which suggests that even the 2DCNN algorithm works on genotype sequences. Moreover, the results generated by the 2DCNN exhibited less variation than those generated by the 1DCNN, thereby offering greater stability. The purpose of this study is to draw the research community's attention to explore encoding schemes for genotype data and machine learning algorithms that can be used on genotype data by changing the representation of the genotype data for case/control classification.

An Early Catch: Multisystem Inflammatory Syndrome in Adults (MIS-A) in a Young Adult Male With COVID-19 Exposure.

While severe acute respiratory syndrome (SARS) is the most common presentation of coronavirus disease 2019 (COVID-19) infection, several short- and long-term complications from COVID-19 infection are also being recognized. One such complication with life-threatening consequences is known as multisystem inflammatory syndrome in adults (MIS-A). While the phenomenon of multisystem inflammatory syndrome in children (MIS-C) is more recognized, the pathophysiology of both presentations remains a mystery currently. Several theories have been put forward however no consensus has been established yet. We present the case of a 20-year-old male who was admitted to the intensive care unit for a multisystem illness characterized by severe biventricular failure, profound shock, and acute liver and kidney injuries. The severity of illness necessitated the treatment with mechanical ventilation, extracorporeal membrane oxygenation (ECMO), vasopressors, and continuous veno-venous hemofiltration (CVVH). The patient was treated with one dose of intravenous immune globulin (IVIG). In association with the foregoing treatment, the patient made dramatic recovery and came off pulmonary, hemodynamic, and renal support within a week and made remarkably quick and full recovery. This case highlights a rare presentation of a COVID-19 complication that requires prompt recognition, supportive care, and empiric treatment that led to a favorable outcome in this case.

Complications of invasive mechanical ventilation in critically Ill Covid-19 patients - A narrative review.

Critically ill COVID-19 patients have to undergo positive pressure ventilation, a non-physiological and invasive intervention that can be lifesaving in severe ARDS. Similar to any other intervention, it has its pros and cons. Despite following Lung Protective Ventilation (LPV), some of the complications are frequently reported in these critically ill patients and significantly impact overall mortality. The complications related to invasive mechanical ventilation (IMV) in critically ill COVID-19 patients can be broadly divided into pulmonary and non-pulmonary. Among pulmonary complications, the most frequent is ventilator-associated pneumonia. Others are barotrauma, including subcutaneous emphysema, pneumomediastinum, pneumothorax, bullous lesions, cardiopulmonary effects of right ventricular dysfunction, and pulmonary complications mimicking cardiac failure, including pulmonary edema. Tracheal complications, including full-thickness tracheal lesions (FTTLs) and tracheoesophageal fistulas (TEFs) are serious but rare complications. Non-Pulmonary complications include neurological, nephrological, ocular, and oral complications.

Unique design approach to realize an O-band laser monolithically integrated on 300†mm Si substrate by nano-ridge engineering.

We introduce a new design space for optimizing III-V devices monolithically grown on Silicon substrates by extending the concept of nano-ridge engineering from binary semiconductors such as GaAs, InAs and GaSb to the ternary alloy InGaAs. This allows controlling the fundamental lattice constant of the fully relaxed ternary nano-ridge which thereby serves as a tunable base for the integration of diverse device hetero-layers. To demonstrate the flexibility of this approach, we realized an O-band nano-ridge laser containing three In0.45Ga0.55As quantum wells, which are pseudomorphically strained to an In0.25Ga0.75As nano-ridge base. The demonstration of an optically pumped nano-ridge laser operating around 1300 nm underlines the potential of this cost-efficient and highly scalable integration approach for silicon photonics.

Patterns of Dyslipidemia Among Acute Coronary Syndrome (ACS) Patients at a Tertiary Care Hospital in Lahore, Pakistan.

INTRODUCTION: Dyslipidemia refers to the presence of abnormalities in lipid parameters. It has become a global issue with a high risk of cardiovascular diseases (CVDs). The aim of the investigation was to find out the pattern and prevalence of dyslipidemia among patients with the acute coronary syndrome (ACS). METHODOLOGY: A cross-sectional study design was used. Data were collected using convenient sampling from 101 patients presenting with ACS, admitted at the critical care unit (CCU) / Rasheeda Begum Cardiac Centre (RBCC) of Shalamar Hospital, during a 12-month period from January 2020 to December 2021. Dyslipidemia is diagnosed by testing the lipid profile when there are one or more abnormal readings of the lipid profile. RESULTS: Nearly 43 (42.6%) had ST-segment elevation myocardial infarction (STEMI), 27 (26.7%) had non-ST segment elevation myocardial infarction (NSTEMI), and 31 (30.7%) were categorized as unstable angina (USA). Overall dyslipidemia was present in 84 (83.2%) patients. The prevalence of dyslipidemia was 55 (65%) in male patients and 29 (34.5%) in female patients. Dyslipidemia was present in 39 (90.7%) patients with STEMI, 25 (80.6%) in the USA, and 20 (74.1%) with NSTEMI. CONCLUSION: The prevalence of dyslipidemia was quite high among ACS patients. The proportion of obese patients was also high in our study. However, dyslipidemia was more frequent in overweight patients.

COVID 19 pandemic: Effect on management of patients with breast cancer; single center retrospective cohort study.

BACKGROUND: (SARS-COV-2) infection, led to a pandemic affecting many countries, resulting in hospitals diverting most of their resources to fight the pandemic. Breast cancer, already a healthcare dilemma, is also affected in this scenario. Our aim was to find out the impact of COVID-19 on presentation of breast cancer stage and its effects on overall onco-surgical management. METHODS: This cohort single-centered retrospective review was carried out at our hospital, over a period of 18 months. Females with known breast cancer were included in the study. Data was collected on performas by a single researcher. Effect of COVID pandemic on presentation stage and its impact on overall management was studied. SPSS 23.0 used for data analysis. A 95% CI was used. Descriptive statistics were presented as range/means. Categorical data was analyzed by Fisher exact test, t-test was applied to numerical data, p value ≤ 0.05 was considered significant. RESULTS: Out of 87 patients presenting with suspicious lump, 69 who had malignancy on histo-pathology were included in study. Twelve out of 69 were COVID positive. Sixty patients presented with advanced stage (≥stage 2b) out of which 21 underwent upstaging of disease due to delay in presentation/management. We found that 9 out of 12 (majority) Covid positive patients had disease upstaging. Overall main reason for delay in presentation was found to be unawareness of disease. CONCLUSION: We concluded that COVID-19 pandemic had no impact on presentation delay, breast cancer management/treatment and disease upstaging as compared to figures available for our population before the pandemic. However, our study showed significant correlation between disease upstaging and COVID status. This led us to reconsider our preformed protocols for COVID positive breast cancer patients. Our results can be used by future researchers to investigate if COVID itself can contributes in patho-physiology of upstaging in breast cancer or not.