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The introduction of Central Bank Digital Currencies (CBDCs) in Pakistan presents a transformative opportunity to enhance the nation's financial landscape by providing a more efficient, inclusive, and secure medium for financial transactions. CBDCs can facilitate cross-border payments, enhance financial inclusion and control for financial crime. However, the success of implementing digital currencies depends very much on society's acceptance of this idea. This study uses a quantitative descriptive design to explore public opinion, awareness and readiness to adopt the Central Bank Digital Currencies in Pakistan. A survey was conducted with respondents from diverse demographic backgrounds to assess their understanding of CBDCs, concern regarding privacy and financial security, and overall willingness to adopt digital currencies issued by the central bank. In this study, the factors that may influence or predict the success of Central Bank Digital Currency in Pakistan's economy are independent variables. The success of CBDC in usage and integration in Pakistan's economy is the dependent variable. Gender and profession are control variables. The analysis that was conducted highlights a generally positive outlook towards CBDC among the survey participants, tempered by notable concerns about trust and sustainability. However, the findings suggest that while there is cautious interest in CBDCs among common Pakistanis, substantial efforts are needed to address the knowledge gap and mitigate concerns about security and privacy. Digital education of the population is one of the key factors in successfully implementing digital currencies in any country, particularly Pakistan. In addition, trust issues remain a critical barrier, especially among those aware of CBDC's benefits. Thus, the results of this study will be useful for policymakers and financial institutions in the context of planning activities for implementing CBDC in Pakistan.

Abstract Background With a wide therapeutic index, efficacy, ease of use, and other neuroprotective and respiratory benefits, caffeine citrate(CC) is currently the drug of choice for preterm neonates (PTNs). Caffeine-induced excessive energy expenditure, diuresis, natriuresis, and other CC-associated potential side-effects (CC-APSEs) result in lower daily-weight gain (WG) in premature neonates. This study aimed to evaluate the risk factors for daily-WG in neonates exposed to different dose regimens of caffeine in ICU. Method This retrospective cohort study included neonates of ≤ 36weeks gestational age (GA) and received CC-therapy. The same participants were followed for data analysis in two postnatal phases: 15–28 and 29–42 days of life (DOL). Based on daily CC-dose, formed group-I (received; standard-doses = 5 mg/kg/day), group-II (received;>5-7 mg/kg/day), and group-III (received;>7 mg/kg/day). Prenatal and postnatal clinical characteristics, CC-regimen, daily-WG, CC-APSEs, and concomitant risk-factors, including daily-caloric intake, Parenteral-Nutrition duration, steroids, diuretics, and ibuprofen exposure, were analyzed separately for group-II and group-III using group-I as standard. Regression analysis was performed to evaluate the risk factors for daily-WG. Results Included 314 PTNs. During 15–28 DOL, the mean-daily-WG(MD-WG) was significantly higher in group-I than group-II [19.9 ± 0.70 g/kg/d vs. 17.7 ± 0.52 p = 0.036] and group-III [19.9 ± 0.70 g/kg/d vs. 16.8 ± 0.73 p 7-10 mg/kg/day (β=-1.36; 95%CI:-1.56,-1.02) were associated with a lower daily-WG. However, during 29-42DOL, this association was present only for > 7-10 mg/kg/day (β=-1.54; 95%CI:-1.66,-1.42). The GA ≤ 27weeks (β=-1.03 95%CI:-1.24, -0.88) was associated with lower daily-WG only during 15-28DOL. During both periods of therapy, higher cumulative-caffeine dose and presence of culture proven sepsis, tachypnea, hyponatremia, and feeding intolerance were significantly associated with lower daily-WG. Conversely, daily kcal intake was found to be linked with an increase in daily-WG in both periods. Conclusion In this study cohort exposure to higher caffeine daily and cumulative doses is associated with lower postnatal daily-WG in PTNs than standard-daily doses, which may be due to its catabolic effects and CC-APSEs.

Recent advances in auditory attention detection from multichannel electroencephalography (EEG) signals encounter the challenges of the scarcity of available online EEG data and the detection of auditory attention with low latency. To this end, we propose a complete deep auditory generative adversarial network auxiliary, named auditory-GAN, designed to handle these challenges while generating EEG data and executing auditory spatial detection. The proposed auditory-GAN system consists of a spectro-spatial feature extraction (SSF) module and an auditory generative adversarial network auxiliary (AD-GAN) classifier. The SSF module extracts the spatial feature maps by learning the topographic specificity of alpha power from EEG signals. The designed AD-GAN network addresses the need for extensive training data by synthesizing augmented versions of original EEG data. We validated the proposed method on the widely used KUL dataset. The model assesses the quality of generated EEG images and the accuracy of auditory spatial attention detection. Results show that the proposed auditory-GAN can produce convincing EEG data and achieves a significant i.e., 98.5% spatial attention detection accuracy for a 10-s decision window of 64-channel EEG data. Comparative analysis reveals that the proposed neural approach outperforms existing state-of-the-art models across EEG data ranging from 64 to 32 channels. The Auditory-GAN model is available at https://github.com/tasleem-hello/Auditory-GAN-/tree/Auditory-GAN.

Background: A fundus first laparoscopic cholecystectomy when performed by an experienced surgeon, provides the same level of safety and durability as an open cholecystectomy. When Calot's triangle cannot be safely dissected during laparoscopic cholecystectomy a rescue treatment is advised. Current revisions stress the importance of intraoperative observations in helping surgeons make a rescue decision and minimize additional harm. Objective: To examines the fundus-first strategy in laparoscopic cholecystectomy in an indistinct Calot's triangle and the patient's clinical outcome. Patients and Methods: This is a prospective study that was done in Rizgary Teaching Hospital from January 1st, 2020 to December 31st, 2022, on 68 cases who underwent laparoscopic cholecystectomy where Calot’s triangle was difficult to distinguish during the operation. Fundus: first dissection of the gall bladder down to the infundibulum and after safe ligation of the gall bladder stump and good hemostasis of the liver bed, the gallbladder is removed via a 10-mm port. This research looked at the duration of operation, postoperative pain, rate of conversion to open surgery and duration of the hospital stay. Results: Among 68 cases of fundus first laparoscopic cholecystectomy most of the patients were female 45(66.17%) and 23(33.82%) were male, majority of cases were between 31 and 50 years old, with a mean age of 39±10.44, and the majority of patients were complaining of inflammation with fibrosis at the site of the cystic duct. The duration of operation ranged from 45-60 minutes in 16(23.52%) and 60-80 minutes in 20(29.41%) cases and 130-140 minutes in 2(2.94%) cases. The majority of our cases remained in the hospital and ambulated on day 3-5, and the main hospital stay was 4.68±1.8 days. There was statistical significance in the age distribution with a P-value 0.04. Conclusion: Fundus first laparoscopic cholecystectomy remains a feasible and safe procedure. Surgeon experience and judgement affects the operative time, conversion rate, morbidity and hospital stay in difficult and obscured Calot’s triangle.

BackgroundThe escalating number of deaths related to opioid usage has intensified the pursuit of non-opioid alternatives for managing chronic pain. It's often observed that psychiatric comorbidities coexist in patients suffering from chronic pain. There are a variety of psychotropic medications that have demonstrated effectiveness in treating both psychiatric symptoms and pain. This systematic review and meta-analysis aim to assess the effectiveness of various psychiatric drugs in managing specific types of chronic pain, including fibromyalgia, neuropathic pain, and chronic low back pain.MethodsA comprehensive search of five major databases was conducted through February 2023 to identify randomized controlled trials (RCTs) that met our inclusion criteria, focusing on outpatients Over 18 years of age with chronic pain. The study assessed the effectiveness of duloxetine, mirogabalin, pregabalin, gabapentin, and tricyclic antidepressants (TCAs), including serotonin-norepinephrine reuptake inhibitors (SNRIs), across various chronic pain conditions such as fibromyalgia, neuropathic pain, and chronic low back pain. The primary outcome measures included pain reduction, improvement in function, and quality of life. Of the 29 RCTs in the systematic review, 20 studies qualified for the meta-analysis. The analysis was stratified by pain type and treatment duration (short-term ≤14 weeks vs. long-term >14 weeks), using Hedge's g standardized mean differences and a random-effects model, along with sensitivity and subgroup analyses.ResultsThe overall short-term intervention effect across all studies was significant (SMD −1.45, 95% CI −2.15 to −0.75, p

Smart grids arose as the largest cyber-physical systems with the integration of sophisticated control, computing, and state-of-the-art communications. Like all cyber-physical systems, the smart grids are vulnerable to malicious cyber assaults due to their enormous dependency on communication networks. Various machine learning-based schemes are being investigated in the industry and academia to develop robust defense mechanisms to counter cyber assaults. However, the curse of high dimensionality, which increases with the escalating evolution of an electric power system, infringes upon the efficiency of machine learning models employed to detect such assaults. To this end, this paper proposes a deep denoising autoencoder (DAE)-based framework for dimensionality reduction that learns salient feature representation for high-dimensional, multi-variant smart grid measurement data collected from the smart grids. The latent space apprehended by DAE is then fed to binary support vector machine (SVM) to determine the assaulted data. Various standard IEEE test cases are employed in simulations. The results show that the proposed scheme learns more robust features that reveal the nonlinear properties exhibited in the smart grid measurements, further leading to improved detection accuracy of the classifier as compared to existing approaches.

IntroductionMetabolic dysfunction-associated steatotic liver disease (MASLD) has become a major cause of end-stage hepatic disease worldwide requiring liver transplantation, whereas cardiovascular disease (CVD) remains the leading cause of morbidity and mortality globally. Development of MASLD and CVD among young adults is understudied. This study aimed to assess CVD risk in healthy young medical university students using lipid-based and body mass index (BMI)-based 30-year Framingham risk scores (FS30) and to evaluate disease burden for asymptomatic patients with MASLD by performing FibroScan.MethodsWe included medical university students aged 18–30 years without any known medical conditions. All participants underwent physical and anthropometric measurements, and completed a questionnaire. Blood samples were collected for the analysis of glycosylated haemoglobin levels, renal and liver function, biomarker analysis to calculate liver fibrosis risk, and subclinical atherosclerosis biomarkers. Liver stiffness measurements (LSM) and controlled attenuation parameter (CAP) values were measured using FibroScan 430 mini to calculate liver fibrosis and steatosis, respectively. FS30 based on body mass index (FS30-BMI) and lipid levels (FS30-Lipid) were also calculated.ResultsOverall, 138 medical students participated in this study after providing informed consent. Using FS30-Lipid and FS30-BMI, CVD risk was identified in two (1.5%; n = 138) and 23 (17.6%; n = 132) individuals, respectively. MASLD fibrosis was identified based on FibroScan LSMs >7.0 kPa in 12 medical students (9.4%, n = 128; 95% CI, 4.7–14.8%). Consumption of coffee and sugary soft drinks were predictive of liver fibrosis. In total, 36 students (28.6%; n = 128) were found to have hepatic steatosis based on FibroScan CAP values >236 dB, and the predictive factors included increased body fat percentage, male sex, and lack of physical activity. Levels of inflammatory biomarkers, such as C-reactive protein and lipids were not elevated in participants with MASLD.DiscussionCVD risk was identified in >17% of young medical students. The frequency of liver fibrosis and steatosis was also high among the participants, indicating that liver damage starts at a relatively early age. Early intervention is needed among young adults via health promotion and lifestyle changes.

Although the flourishing of the Internet of Things and artificial intelligence has accelerated the development of wearable smart bioelectronics, heavy reliance on external power remains a problem that needs to be solved. Thermoelectric materials have emerged as a promising solution, efficiently converting body heat into electrical energy to provide a stable and unrestricted power supply for wearables. Moreover, in the field of wearable thermoelectric biosensing, where flexibility is highly demanded, hydrogels with excellent electrical conductivity, flexibility and biocompatibility through structural and compositional optimization have become ideal materials for constructing biosensors and meet the diverse application needs of wearable bioelectronics. This article systematically reviews the latest research progress on thermoelectric gels for self-powered wearable biosensing, including the principles of thermoelectric operation, as well as the preparation, design, and application of thermoelectric hydrogels. The current state of thermoelectric gel-based wearable applications in the fields of temperature sensing, strain sensing, temperature-strain synergistic sensing, respiratory monitoring, and sweat analysis are displayed in the article. Finally, the paper summarizes the current challenges and prospects of thermoelectric gels in self-powered wearable bioelectronics, encouraging the rapid application and realization of thermoelectric gel-based smart wearables.

Background and objective: Chronic anal fissure is a common health problem related to high morbidity. The chemical option for treating chronic anal fissures is highly preferred. Methods: A clinical prospective comparative study was carried out in the General Surgery Outpatient Department in Rizgary Teaching Hospital, Erbil, KRG, during the period from January 2019 to December 2021,on a sample of 100 patients randomly recruited patients with chronic anal fissures divided into two groups with 50 participants each: the DTZ group, who received 2% diltiazem ointment, and the GTN group, who used 0.2% glyceryl trinitrate ointment twice daily for 6 consecutive weeks. The aim of the study is to compare the efficacy and effects of topical Diltiazem (DTZ) and topical glyceryl trinitrate (GTN) in the management of chronic anal fissures. Results: There was a highly significant association between fissure healing at the 4th week and patients treated with DTZ (P

Background: Wilms Tumor (WT) is a highly curable cancer if treatment is appropriate and timely. The outcomes and prognostic factors in a large low- and middle-income country (LMIC) tertiary center were assessed. Materials and methods: Retrospective review of data of all patients, 0–15 years diagnosed between 2010 and 2020 with WT. Kaplan Meier curves were used for survival analysis, and the chi-square test was used for multivariate analysis. Results: Of the 40 patients enrolled (median age: 38 months) in the cohort, 10 had metastatic disease. The most common site of metastasis was lungs (6/10). Nine (22.5%) abandoned treatment. Large tumor (>500 ml) volume was found in half the patients at diagnosis. The majority of patients were treated per the SIOP approach. Out of 34 who went for surgery, 31 received neoadjuvant chemotherapy with tumor shrinkage to less than 500 ml in 26/31 (80%). Maximum tumor shrinkage was observed in the SIOP low-risk group (p

Abstract: As it stands, Spigelian hernias are rare and generally overlooked or misdiagnosed. We report a case in which a Spigelian hernia presented to us as a painless abdominal swelling in a 39 year old obese female with no known co-morbids The hernia appeared as swelling in the right lower quadrant, which was non tender at the time of admission and showed no signs of strangulation. Diagnosis of the hernia was made on CT-scan and an open technique was adapted to repair it. The post op period was insignificant in the patient. Key Words: Spiglelian Hernia, Hernia, Spigelian Fascia Defect.

Background and purposeWe proposed an artificial neural network model to predict radiobiological parameters for the head and neck squamous cell carcinoma patients treated with radiation therapy. The model uses the tumor specification, demographics, and radiation dose distribution to predict the tumor control probability and the normal tissue complications probability. These indices are crucial for the assessment and clinical management of cancer patients during treatment planning.MethodsTwo publicly available datasets of 31 and 215 head and neck squamous cell carcinoma patients treated with conformal radiation therapy were selected. The demographics, tumor specifications, and radiation therapy treatment parameters were extracted from the datasets used as inputs for the training of perceptron. Radiobiological indices are calculated by open-source software using dosevolume histograms from radiation therapy treatment plans. Those indices were used as output in the training of a single-layer neural network. The distribution of data used for training, validation, and testing purposes was 70, 15, and 15%, respectively.ResultsThe best performance of the neural network was noted at epoch number 32 with the mean squared error of 0.0465. The accuracy of the prediction of radiobiological indices by the artificial neural network in training, validation, and test phases were determined to be 0.89, 0.87, and 0.82, respectively. We also found that the percentage volume of parotid inside the planning target volume is the significant parameter for the prediction of normal tissue complications probability.ConclusionWe believe that the model has significant potential to predict radiobiological indices and help clinicians in treatment plan evaluation and treatment management of head and neck squamous cell carcinoma patients.

Habenaria aitchisonii Reichb was analyzed in this research, including its chemical composition and its in vitro antioxidant, anti-inflammatory, acute oral toxicity, and antinociceptive activity. The chloroform and ethyl acetate fractions were found to be the most powerful based on in vitro antioxidant, anti-inflammatory, and analgesic assays. The acute oral toxicity of the crude methanolic extract was determined before in vivo studies. The acetic acid and formalin tests were used to measure the antinociceptive effect, and the potential mechanisms involved in antinociception were explored. The carrageenan-induced paw edema test was used to examine the immediate anti-inflammatory effect, and many phlogistic agents were used to determine the specific mechanism. Furthermore, for ex vivo activities, the mice were sacrificed, the forebrain was isolated, and the antioxidant levels of glutathione (GSH), superoxide dismutase (SOD), thiobarbituric acid reactive substances (TBARS) and catalase (CAT) were estimated using a UV spectrophotometer. No toxicity was seen at oral dosages up to 3,000 mg/kg. The antinociceptive impact was much higher than the standard drug. Both the inflammatory and neurogenic phases of the formalin experiment revealed an analgesic effect in the chloroform and ethyl acetate fractions. In carrageenan anti-inflammatory assays, the chloroform fraction (Ha.Chf) was the most potent fraction. We further studied the GC-MS of crude plant extract and found a total of 18 compounds. In the anti-inflammatory mechanism, it was observed that the Ha.Chf inhibits the COX-2 as well as 5-LOX pathways. The results exhibited that this species is a good source of phytocomponents like germacrone, which can be employed as a sustainable and natural therapeutic agent, supporting its traditional use in folk medicine for inflammatory conditions and pain.

Groundwater pollution is now a significant threat to the ecosystem. The untreated disposal of municipal and industrial wastes through drains into water bodies (rivers, lakes, and canals) is the primary source of contamination. The highly contaminated water harms individual health. In this study, total dissolved solids (TDS) and arsenic (As) mobilization were explored depthwise from a drilled borehole at main Raiwind Road, Thokar Niaz Baig, near River Ravi, Lahore. The concentrations of TDS and As in the aquifer indicate that the top shallow aquifer bearing high salts occurred due to local inputs of waste effluents discharged into the River Ravi. In contrast, at deep shallow aquifers, enrichment of As with elevated pH results from the geochemical oxidative dissolution process. The results showed that natural and anthropogenic activities influence groundwater throughout the depth. The TDS and As concentrations were found above the World Health Organisation (WHO) guideline values at some depth intervals, which are harmful to human health. HIGHLIGHTS Depthwise evaluation of total dissolved solids and arsenic from drilled borehole is performed.; Concentration of TDS and arsenic varied from shallow to deep groundwater.; Groundwater quality is changing due to natural and anthropogenic activities.;

In this work, a series of hierarchical flower-like magnesium oxide (MgO) adsorbents were successfully fabricated in a cetyltrimethylammonium bromide (CTAB) assisted solvothermal route using hexamethylenetetramine (HMTA) as a precipitating agent. Effects of CTAB feeding amount on the structure, morphology, pore structure, and corresponding adsorption behavior were investigated. The hierarchical gardenias flower-like MgO demonstrated a surface area of 336.54 m2·g−1 at a minimum ratio of the CTAB/Mg2+ was 0.02 in the reaction system. The hierarchical MgO phosphate removal capacity was 348.32 mg·g−1, which followed the pseudo-second-order and Freundlich isotherm model obtained from the large surface area and appropriate pore size. The value of n also suggests the feasible nature of phosphate adsorption under the examined conditions. Indeed, this CTAB assisted solvothermal method can provide a new understanding to tune the desired properties of a material by merely adjusting the reaction parameters of MgO.

Abstract To meet the rising requirement of photovoltaic compounds for modernized hi-tech purpose, we designed six new molecules (DTPD1-DTPD6) from banana shaped small fullerene free chromophore (DTPR) by structural tailoring at terminal acceptors. Frontier molecular orbitals (FMOs), density of states (DOS), open circuit voltage (V oc), transition density matrix (TDM) analysis, optical properties, reorganization energy value of hole and electron were determined utilizing density function theory (DFT) and time-dependent density function theory (TD-DFT) approaches, to analyze photovoltaic properties of said compounds. Band gap contraction (∆E = 2.717–2.167 eV) accompanied by larger bathochromic shift (λ max = 585.490–709.693 nm) was observed in derivatives contrary to DTPR. The FMOs, DOS and TDMs investigations explored that central acceptor moiety played significant role for charge transformation. The minimum binding energy values for DTPD1-DTPD6 demonstrated the higher exciton dissociation rate with greater charge transferal rate than DTPR, which was further endorsed by TDM and DOS analyses. A comparable V oc (1.49–2.535 V) with respect to the HOMOPBDBT–LUMOacceptor for entitled compounds was investigated. In a nutshell, all the tailored chromophores can be considered as highly efficient compounds for promising OSCs with a good V oc response.

Artificial Intelligence and Deepfake Technologies have brought a new dimension to the generation of fake data, making it easier and faster than ever before—this fake data could include text, images, sounds, videos, etc. This has brought new challenges that require the faster development of tools and techniques to avoid fraudulent activities at pace and scale. Our focus in this research study is to empirically evaluate the use and effectiveness of deep learning models such as Convolutional Neural Networks (CNNs) and Patch-based Neural Networks in the context of successful identification of real and fake images. We chose the healthcare domain as a potential case study where the fake medical data generation approach could be used to make false insurance claims. For this purpose, we obtained publicly available skin cancer data and used recently introduced stable diffusion approaches—a more effective technique than prior approaches such as Generative Adversarial Network (GAN)—to generate fake skin cancer images. To the best of our knowledge, and based on the literature review, this is one of the few research studies that uses images generated using stable diffusion along with real image data. As part of the exploratory analysis, we analyzed histograms of fake and real images using individual color channels and averaged across training and testing datasets. The histogram analysis demonstrated a clear change by shifting the mean and overall distribution of both real and fake images (more prominent in blue and green) in the training data whereas, in the test data, both means were different from the training data, so it appears to be non-trivial to set a threshold which could give better predictive capability. We also conducted a user study to observe where the naked eye could identify any patterns for classifying real and fake images, and the accuracy of the test data was observed to be 68%. The adoption of deep learning predictive approaches (i.e., patch-based and CNN-based) has demonstrated similar accuracy (~100%) in training and validation subsets of the data, and the same was observed for the test subset with and without StratifiedKFold (k = 3). Our analysis has demonstrated that state-of-the-art exploratory and deep-learning approaches are effective enough to detect images generated from stable diffusion vs. real images.