bluuebunny/crossref_metadata_2025 huggingface dataset:
Shear wave splitting and seismic velocity structure in the focal area of the earthquake swarm and their relation with earthquake swarm activity in the Noto Peninsula, central Japan
Authors: Okada Tomomi , Savage Martha K. , Sakai Shin’ichi , Yoshida Keisuke , Uchida Naoki , Takagi Ryota , Kimura Shuutoku , Hirahara Satoshi , Tagami Ayaka , Fujimura Ryotaro , Matsuzawa Toru , Kurashimo Eiji , Hiramatsu Yoshihiro
Published: February 2024

Seismic activity in the Noto region of Ishikawa Prefecture, central Japan, has increased since August 2020 and has continued as of August 2023. Stress changes due to subsurface sources and increases in fluid pressure have been discussed as the causes of the seismic activity increase. In this study, S-wave polarization anisotropy was investigated by S-wave splitting analysis using temporary and permanent stations located in the epicenter area. We also investigated the seismic wave velocity structure in the source region by analyzing seismic wave velocity tomography. The fast orientations of anisotropy (fast shear wave oscillation direction, FSOD) were generally NW–SE in the southern part of the focal area and east–west in the northern part. The NW–SE anisotropy generally coincides with the direction of the maximum horizontal compression axis, both near the surface and at earthquake depths. Therefore, stress-induced anisotropy can be the cause of the observed NW–SE anisotropy. On the other hand, faults with strike directions generally east–west have been identified, and structural anisotropy may be the cause of the observed east–west anisotropy. We examined the time variation of anisotropy at N.SUZH, one of the permanent stations. No significant time variation was observed in the FSOD. Larger anisotropy was observed, particularly for the activity in the western part of the focal area, from about June–September 2021 compared to the previous period. A high Vp/Vs region was identified beneath the focal area, at a depth of 18 km. This high Vp/Vs region has slightly larger P-wave velocities than the surrounding area. Since Tertiary igneous rocks are distributed in the target area, the high Vp/Vs region may represent a Tertiary magma reservoir, suggesting that fluids released through the old magma reservoir are involved in this seismic swarm. This seismic activity started in the southern part of the area, where relatively immature fault structure exists, where stress-induced anisotropy is distributed, and where high Vp/Vs regions suggestive of fluid at depth are identified. Subsequently, seismicity became more active in the northern part, where structural anisotropy with well-developed fault structures is distributed.

Graphical Abstract
3D convolutional neural networks uncover modality-specific brain-imaging predictors for Alzheimer’s disease sub-scores
Authors: Ning Kaida , Cannon Pascale B. , Yu Jiawei , Shenoi Srinesh , Wang Lu ,   Alzheimer’s Disease Neuroimaging Initiative, Sarkar Joydeep
Published: February 2024

Different aspects of cognitive functions are affected in patients with Alzheimer’s disease. To date, little is known about the associations between features from brain-imaging and individual Alzheimer’s disease (AD)-related cognitive functional changes. In addition, how these associations differ among different imaging modalities is unclear. Here, we trained and investigated 3D convolutional neural network (CNN) models that predicted sub-scores of the 13-item Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS–Cog13) based on MRI and FDG–PET brain-imaging data. Analysis of the trained network showed that each key ADAS–Cog13 sub-score was associated with a specific set of brain features within an imaging modality. Furthermore, different association patterns were observed in MRI and FDG–PET modalities. According to MRI, cognitive sub-scores were typically associated with structural changes of subcortical regions, including amygdala, hippocampus, and putamen. Comparatively, according to FDG–PET, cognitive functions were typically associated with metabolic changes of cortical regions, including the cingulated gyrus, occipital cortex, middle front gyrus, precuneus cortex, and the cerebellum. These findings brought insights into complex AD etiology and emphasized the importance of investigating different brain-imaging modalities.
Examining the influence of thermal effects on solar cells: a comprehensive review
Authors: Shaker Lina M. , Al-Amiery Ahmed A. , Hanoon Mahdi M. , Al-Azzawi Waleed K. , Kadhum Abdul Amir H.
Published: February 2024

Solar energy has emerged as a pivotal player in the transition towards sustainable and renewable power sources. However, the efficiency and longevity of solar cells, the cornerstone of harnessing this abundant energy source, are intrinsically linked to their operating temperatures. This comprehensive review delves into the intricate relationship between thermal effects and solar cell performance, elucidating the critical role that temperature plays in the overall efficacy of photovoltaic systems. The primary objective of this review is to provide a comprehensive examination of how temperature influences solar cells, with a focus on its impact on efficiency, voltage, current output, and overall stability. By synthesizing existing knowledge and exploring recent advances in the field, we aim to elucidate the underlying mechanisms of thermal effects and offer insights into mitigating their adverse consequences. Our review encompasses a thorough discussion of the fundamentals of solar cells, including their operation and various types, before delving into the intricacies of thermal effects. We present an overview of experimental techniques for thermal analysis, factors influencing temperature variations, and strategies to alleviate thermal stresses. Additionally, we offer real-world case studies and discuss future trends and research directions, providing a comprehensive roadmap for advancing solar cell technology. In an era where the harnessing of solar energy has become increasingly vital, understanding and addressing thermal effects are imperative to maximize the efficiency and longevity of solar cells. This review article serves as a valuable resource for researchers, engineers, and policymakers by shedding light on the significance of thermal effects on solar cell performance and guiding the pursuit of innovative solutions in the quest for more efficient and sustainable photovoltaic systems.
Echoes through time: amazing inferences from a fossil bat
Authors: Greville Lucas J. S. , Hou Lily , Kumbhani Harry A. W. , Nogueira e Figueira Beatriz , Vanderwolf Karen J. , Leys Ryan A. C. , Sivatheesan Mathumy , Pianta Thomas P. , McGuire Liam P.
Published: February 2024

Sister to the Chiroptera crown-clade, the 50 million year old Vielasia sigei is suggested to have used laryngeal echolocation based on morphometric analyses. We discuss how Vielasia’s discovery influences our understanding of the evolution of echolocation in bats and the insights fossils provide to the lives of extinct species.
Optical coherence tomography detection of retinal neural loss in patients with tuberous sclerosis
Authors: Dias Paula Basso , Linhares Anna Carolina Badotti , Urzedo Ana Barbara Dias Lopes , Preti Rony Carlos , Zacharias Leandro Cabral , Cunha Leonardo Provetti , Monteiro Mário Luiz Ribeiro , Hokazono Kenzo
Published: February 2024

Purpose

Tuberous Sclerosis (TS) is a rare, multisystem genetic disease caused by mutations in the TSC1 and TSC2 genes, leading to abnormalities in cell differentiation and proliferation. This study aimed to evaluate the neural integrity of individuals with TS by using Optical Coherence Tomography (OCT) to examine the peripapillary retinal nerve fiber layer (RNFL) thickness and the macular thickness in patients with TS and to compare with healthy controls.

Methods

Peripapillary and macular OCT scans (Optopol Revo NX SD OCT) were performed on 41 eyes from 22 TS patients, divided into two groups based on the presence of retinal hamartomas, and compared to 20 eyes from a control group. The average peripapillary RNFL thickness was measured for each quadrant. The macular total thickness and ganglion cell layer (GCL) + inner plexiform layer (IPL) thickness were measured based on the Early Treatment Diabetic Retinopathy Study (ETDRS) map. All measurements were then compared between the groups and controls.

Results

The TS group showed significantly reduced RNFL thickness and macular thickness when compared to the control group. Specifically, patients with retinal hamartomas exhibited an even more pronounced thinning of both RNFL and macular thickness.

Conclusions

These findings suggest that TS patients undergo significant changes in retinal neurodevelopment and experience axonal loss. This finding may have significant prognostic utility regarding central nervous system degeneration in TS, particularly among patients with retinal hamartomas. OCT may serve as a valuable tool for assessing axonal structural abnormalities in TS patients.

Trial Registration Number: Not applicable.
Development assistance, donor–recipient dynamic, and domestic policy: a case study of two health interventions supported by World Bank–UK and Global Fund in China
Authors: Huang Aidan , Zhao Yingxi , Cao Chunkai , Lyu Mohan , Tang Kun
Published: February 2024

Background

This study views sustainability after the exit of development assistance for health (DAH) as a shared responsibility between donors and recipients and sees transitioning DAH-supported interventions into domestic health policy as a pathway to this sustainability. It aims to uncover and understand the reemergent aspects of the donor–recipient dynamic in DAH and how they contribute to formulating domestic health policy and post-DAH sustainability.

Methods

We conducted a case study on two DAH-supported interventions: medical financial assistance in the Basic Health Services Project supported by the World Bank and UK (1998–2007) and civil society engagement in the HIV/AIDS Rolling Continuation Channel supported by the Global Fund (2010–2013) in China. From December 2021 to December 2022, we analyzed 129 documents and interviewed 46 key informants. Our data collection and coding were guided by a conceptual framework based on Walt and Gilson’s health policy analysis model and the World Health Organization’s health system building blocks. We used process tracing for analysis.

Results

According to the collected data, our case study identified three reemergent, interrelated aspects of donor–recipient dynamics: different preferences and compromise, partnership dialogues, and responsiveness to the changing context. In the case of medical financial assistance, the dynamic was characterized by long-term commitment to addressing local needs, on-site mutual learning and understanding, and local expertise cultivation and knowledge generation, enabling proactive responses to the changing context. In contrast, the dynamic in the case of HIV/AIDS civil society engagement marginalized genuine civil society engagement, lacked sufficient dialogue, and exhibited a passive response to the context. These differences led to varying outcomes in transnational policy diffusion and sustainability of DAH-supported interventions between the cases.

Conclusions

Given the similarities in potential alternative factors observed in the two cases, we emphasize the significance of the donor–recipient dynamic in transnational policy diffusion through DAH. The study implies that achieving post-DAH sustainability requires a balance between donor priorities and recipient ownership to address local needs, partnership dialogues for mutual understanding and learning, and collaborative international–domestic expert partnerships to identify and respond to contextual enablers and barriers.
Continuous improvement through differential trajectories of individual minimal disease activity criteria with guselkumab in active psoriatic arthritis: post hoc analysis of a phase 3, randomized, double-blind, placebo-controlled study
Authors: Coates Laura C. , Rahman Proton , Mease Philip J. , Shawi May , Rampakakis Emmanouil , Kollmeier Alexa P. , Xu Xie L. , Chakravarty Soumya D. , McInnes Iain B. , Tam Lai-Shan
Published: February 2024

Background

To explore the trajectory of, and factors contributing to, achievement of individual criteria of minimal disease activity (MDA) in patients with active psoriatic arthritis (PsA) treated with guselkumab.

Methods

The Phase 3, randomized, placebo-controlled DISCOVER-2 study enrolled adults (N = 739) with active PsA despite standard therapies who were biologic/Janus kinase inhibitor-naive. Patients were randomized 1:1:1 to guselkumab 100 mg every 4 weeks; guselkumab 100 mg at week 0, week 4, then every 8 weeks; or placebo. In this post hoc analysis, patients randomized to guselkumab were included and pooled (N = 493). Longitudinal trajectories of achieving each MDA criterion through week 100 were derived using non-responder imputation. Time to achieve each criterion was estimated with Kaplan-Meier analysis. Multivariate regression for time to achieve each criterion (Cox regression) and achievement at week 100 (logistic regression) was used to identify contributing factors.

Results

Continuous improvement across all MDA domains was shown over time. ~70% of patients achieved near remission in swollen joint count (SJC), Psoriasis Area and Severity Index (PASI), and enthesitis through week 100. Median times to achieve individual criteria differed significantly (p < 0.0001), with SJC ≤ 1 (20 weeks), PASI ≤ 1 (16 weeks), and ≤ 1 tender entheses (16 weeks) being faster than patient-reported criteria (pain ≤ 15 mm, patient global assessment of arthritis and psoriasis ≤ 20 mm, Health Assessment Questionnaire-Disability Index ≤ 0.5) and tender joint count ≤ 1. Higher baseline domain scores, older age, worse fatigue, and increased body mass index were significant predictors of longer time to achieve minimal levels of disease activity assessed via patient-reported criteria.

Conclusions

Substantial proportions of guselkumab-treated patients achieved individual MDA criteria, each showing continuous improvement through week 100, although with distinct trajectories. Median times to achieve physician-assessed MDA criteria were significantly faster compared with patient-driven criteria. Identification of modifiable factors affecting the time to achieve patient-reported criteria has the potential to optimize the achievement and sustainability of MDA in the clinic via a multidisciplinary approach to managing PsA, involving both medical and lifestyle interventions.

Trial registration number

NCT03158285.

Trial registration date

May 16, 2017.
Extension of a sharp-interface immersed-boundary method for simulating parachute inflation
Authors: Zhang Yang , Pu Tianmei , Jia He , Wu Shiqing , Zhou Chunhua
Published: February 2024

In this work, the sharp-interface immersed boundary (IB) method proposed by Mittal et al. (J Comput Phys 227(10):4825–4852, 2008) is extended to fluid-structure-interaction (FSI) simulation of parachute inflation by utilizing several open-source tools. The method employs a Cartesian-grid ghost-cell methodology to accurately represent the immersed boundary, and it is suitable for solving moving-boundary flows with arbitrarily complex geometries. The finite-element code CalculiX is employed to solve the structural dynamics of the parachute system. The IB flow solver is coupled with CalculiX in a minimally-invasive manner using the multi-physics coupling library preCICE. The implicit fluid-structure coupling together with the Aitken adaptive under-relaxation scheme is considered to improve the numerical accuracy and stability. The developed approach is validated by a benchmark FSI case. Numerical experiments on the inflation process of several typical parachutes are further conducted. The breathing process, flow structure, canopy displacement and drag coefficient are analyzed to demonstrate the applicability of the present approach for simulating parachute inflation.
A modified frailty index predicts complication, readmission, and 30-day mortality following the revision total hip arthroplasty
Authors: Momtaz David , Okpara Shawn , Martinez Armando , Cushing Tucker , Ghali Abdullah , Gonuguntla Rishi , Kotzur Travis , Duruewuru Anthony , Harris Madison , Seifi Ali , Harrington Melvyn
Published: February 2024

Introduction

This study aimed to develop a modified frailty index (MFI) to predict the risks of revision total hip arthroplasty (THA).

Methods

Data from the American College of Surgeons - National Surgical Quality Improvement Program were analyzed for patients who underwent revision THA from 2015 to 2020. An MFI was composed of the risk factors, including severe obesity (body mass index > 35), osteoporosis, non-independent function status prior to surgery, congestive heart failure within 30 days of surgery, hypoalbuminemia (serum albumin < 3.5), hypertension requiring medication, type 1 or type 2 diabetes, and a history of chronic obstructive pulmonary disease or pneumonia. The patients were assigned based on the MFI scores (MFI0, no risk factor; MFI1, 1–2 risk factors; MFI2, 3–4 risk factors; and MFI3, 5+ risk factors). Confidence intervals were set at 95% with a P value less than or equal to 0.05 considered statistically significant.

Results

A total of 17,868 patients (45% male, 55% female) were included and had an average age of 68.5 ± 11.5 years. Odds of any complication, when compared to MFI0, were 1.4 (95% CI [1.3, 1.6]) times greater for MFI1, 3.2 (95% CI [2.8, 3.6]) times greater for MFI2, and 10.8 (95% CI [5.8, 20.0]) times greater for MFI3 (P < 0.001). Odds of readmission, when compared to MFI0, were 1.4 (95% CI [1.3, 1.7]) times greater for MFI1, 2.5 (95% CI [2.1, 3.0]) times greater for MFI2, and 4.1 (95% CI [2.2, 7.8]) times greater for MFI3 (P < 0.001).

Conclusion

Increasing MFI scores correlate with increased odds of complication and readmission in patients who have undergone revision THA. This MFI may be used to predict the risks after revision THA.
Influence of various cooking treatments and extraction solvents on bioactive compounds and antioxidant capacities of bottle gourd (Lagenaria siceraria) fruit in India
Authors: Yadav Roshanlal , Yadav Baljeet S. , Yadav Ritika
Published: February 2024

Bottle gourd is a good source of health-beneficial bioactive substances. This research aimed at evaluating the effect of different cooking methods (pressure cooking, microwaving, and frying) and extraction solvents (methanol, ethanol and butanol) on bottle gourd fruit phenolic compounds and antioxidant capacity. The quantitative estimation of polyphenolic compounds, flavonoids and tannins was estimated by spectrophotometric methods. The antioxidative properties were evaluated using ferric thiocyanate (FTC), thiobarbituric acid (TBA), ferric reducing antioxidant power (FRAP) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) tests using the standard methods. Moreover, quantification of individual polyphenols was carried out by high-performance thin layer chromatography (HPTLC) technique. Frying and pressure-cooking thermal treatments were observed to be the best methods for retaining bioactive molecules. When compared to the raw counterpart, the retention level of total phenol content (TPC) in pressure cooked and fried samples was 23.8% and 13.3%, respectively. Similarly, antioxidant capacity in terms of FRAP was observed to increase by 47.26% after pressure cooking and 28.50% after microwave cooking in respect to the raw sample. The HPTLC results showed that this fruit has high antioxidant capacity and is rich in phenolic acid [gallic(17.83 mg g−1dwb), p-coumaric (6.70 mg g−1dwb)and vanillic (60.66 mg g−1dwb] and flavonoids [quercetin (24.64 mg g−1dwb) and myrecetin (20.73 mg g−1dwb].The chromatography indicated destruction and transformation of new phenolic compounds due to heat treatments. The correlation analysis revealed that flavonoids were much more responsible for their antioxidant activity. Cooking methods and extraction solvents affected the retention and recovery of polyphenolic compounds available in bottle gourd fruit.These findings offer valuable information for optimizing cooking techniques to preserve and enhance the nutritional and antioxidant properties of bottle gourd, making it a valuable fruit for a healthy diet. Pressure cooking could be the most suitable cooking treatment as far as retention of bioactive compounds like phenolic acids is concerned.

Graphical Abstract
Overcoming Klebsiella pneumoniae antibiotic resistance: new insights into mechanisms and drug discovery
Authors: Huy Tran Xuan Ngoc
Published: February 2024

Background

Due to antibiotic overuse, numerous organisms have evolved multidrug resistance, a global health crisis. Klebsiella pneumoniae (K. pneumoniae) causes respiratory, biliary, and urinary infections. It initially impacts individuals with impaired immune systems.

Main body of abstract

The treatment of K. pneumoniae clones poses significant challenges, highlighting the need for new, more effective pathogen control methods. Our understanding of the natural or acquired antibiotic resistance mechanisms in K. pneumoniae is inadequate. Therefore, it is crucial to continue searching for novel compounds to fight against K. pneumoniae and to understand its antibiotic resistance processes fully.

Short conclusion

This review covers K. pneumoniae antibiotic resistance in detail. The review also identifies the molecular components of natural or acquired K. pneumoniae antibiotic resistance mechanisms. This study further digs into the novel pharmacological targets to offer therapy insights.
Long-term efficacy of a continuity-of-care treatment model for patients with severe mental illness who transition from in-patient to out-patient services
Authors: Maoz Hagai , Sabbag Rony , Mendlovic Shlomo , Krieger Israel , Shefet Daphna , Lurie Ido
Published: February 2024

Background

Despite its significance, ensuring continuity of care demands substantial resources, which might not be readily accessible in many public healthcare systems. Studies indicate that continuity of care remains uncertain in numerous healthcare systems.

Aims

This study aimed to assess the effectiveness of a continuity-of-care model for patients with severe mental illness (SMI), providing seamless treatment from discharge from a closed ward to subsequent psychiatric, psychological and rehabilitation services.

Method

Data from patients discharged before (1 January to 31 December 2018) and after (1 June 2021 to 31 May 2022) full implementation of the model were analysed and compared in terms of average duration of hospital stay, emergency department visits within 90 days of discharge, readmission rate within a year post-discharge and initiation of rehabilitation process.

Results

In the post-implementation period (n = 482), the average admission time significantly decreased from 30.51 ± 29.72 to 26.77 ± 27.89 days, compared with the pre-implementation period (n = 403) (P = 0.029). Emergency department visits within 90 days following discharge decreased from 38.70 to 26.35% of discharged patients (P < 0.001). The rate of readmission decreased from 50.9 to 44.0% (P = 0.041) for one readmission and from 28.3 to 22.0% (P = 0.032) for two readmissions in the year following discharge. Additionally, the proportion of patients entering formal rehabilitation increased from 7.94 to 12.03% (P = 0.044).

Conclusions

This study highlights the effectiveness of a continuity-of-care model spearheaded by senior psychiatrists and involving paramedical personnel. These findings underscore the significant potential of the model to substantially enhance mental health services and outcomes. Moreover, they emphasise its relevance for patients, clinicians and policy makers.
Magnetic Resonance Imaging–Guided Frameless Stereotactic Injections of the Bilateral Cerebellar Dentate Nuclei in Nonhuman Primates: Technical Note
Authors: Qiu Liming , Xu Emily , Chambule Sydney , LaTourette Philip , Dyer Cecilia D. , Wallace Chelsea K. , Donocoff Rachel , Wilson James M. , Lucas Timothy H. , Chen H. Isaac
Published: February 2024

BACKGROUND AND OBJECTIVES:

Nonhuman primates (NHPs) are important preclinical models for evaluating therapeutics because of their anatomophysiological similarities to humans, and can be especially useful for testing new delivery targets. With the growing promise of cell and gene therapies for the treatment of neurological diseases, it is important to ensure the accurate and safe delivery of these agents to target structures in the brain. However, a standard guideline or method has not been developed for stereotactic targeting in NHPs. In this article, we describe the safe use of a magnetic resonance imaging–guided frameless stereotactic system to target bilateral cerebellar dentate nuclei for accurate, real-time delivery of viral vector in NHPs.

METHODS:

Seventeen rhesus macaques (Macaca mulatta) underwent stereotactic surgery under real-time MRI guidance using the ClearPoint® system. Bilateral cerebellar dentate nuclei were targeted through a single parietal entry point with a transtentorial approach. Fifty microliters of contrast-impregnated infusate was delivered to each dentate nucleus, and adjustments were made as necessary according to real-time MRI monitoring of delivery. Perioperative clinical outcomes and postoperative volumes of distribution were recorded.

RESULTS:

All macaques underwent bilateral surgery successfully. Superficial pin site infection occurred in 4/17 (23.5%) subjects, which resolved with antibiotics. Two episodes of transient neurological deficit (anisocoria and unilateral weakness) were recorded, which did not require additional postoperative treatment and resolved over time. Volume of distribution of infusate achieved satisfactory coverage of target dentate nuclei, and only 1 incidence (2.9%) of cerebrospinal fluid penetration was recorded. Mean volume of distribution was 161.22 ± 39.61 mm3 (left, 173.65 ± 48.29; right, 148.80 ± 23.98).

CONCLUSION:

MRI-guided frameless stereotactic injection of bilateral cerebellar dentate nuclei in NHPs is safe and feasible. The use of this technique enables real-time modification of the surgical plan to achieve adequate target coverage and can be readily translated to clinical use.
Climate Change and Diabetes Mellitus - Emerging Global Public Health Crisis: Observational Analysis
Authors: Meo Sultan Ayoub , Meo Anusha Sultan
Published: February 2024

Climate change is the most pressing challenge of the 21st century. It’s immediate impacts on the environment are extreme weather conditions such as heatwaves, storms, rains, floods, sealevel rise, the disruption of crops, agricultural systems, water, vector-borne diseases, and ecosystems. The weather-related disasters disturbed the natural biological environment and dislocated millions of people from their homes. The extreme weather conditions caused the deaths of about two million people and $4.3 trillion in economic loss over the past half a century, and 90% of deaths were reported from developing countries. It has also been predicted that between 2030 and 2050, climate change is presumed to cause about 250,000 additional deaths per annum. The rapid rise in temperatures, frequencies of heat waves, wildfires, storms, and other weather extremes conditions could affect human health in many ways. The one-degree Celsius rise in outdoor temperature causes over 100,000 new cases of diabetes mellitus per annum. Climate change compromised body metabolism, vasodilation, sweating, insulin resistance and cause Type-2 diabetes mellitus and gestational diabetes Mellitus.

doi: https://doi.org/10.12669/pjms.40.4.8844

How to cite this: Meo SA, Meo AS. Climate Change and Diabetes Mellitus - Emerging Global Public Health Crisis: Observational Analysis. Pak J Med Sci. 2024;40(4):559-562. doi: https://doi.org/10.12669/pjms.40.4.8844

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Effects of glycated serum protein, homocysteine, and cystatin-C levels on pregnancy outcomes in patients with gestational diabetes mellitus
Authors: Chen Yuhua , Li Chunfang , Lei Yurong , Zhang Fen
Published: February 2024

Objective: To explore the effects of serum glycated serum protein (GSP), homocysteine (Hcy) and cystatin-C (Cys-C) levels on pregnancy outcomes in patients with gestational diabetes mellitus (GDM).

Methods: Retrospective selection of 247 pregnant women who underwent normal prenatal examinations in The Yan’an People’s Hospital from January 2020 to May 2022 were included in this retrospective study. Among them, 119 were pregnant women with diabetes (GDM-group) and 128 were pregnant women with normal blood glucose (Normal-group). The levels of serum GSP, HCY, CYS-C, and incidence of adverse pregnancy outcomes were compared between the two groups. The clinical value of levels of serum GSP, Hcy, and Cys-C in predicting adverse pregnancy outcomes were analyzed.

Results: Compared with the Normal-group, the overall incidence of adverse pregnancy outcomes, serum GSP, Hcy, and Cys-C levels in the GDM-group were significantly higher (p<0.05). Logistic regression analysis showed that the levels of GSP, Hcy, and Cys-C were independent risk factors for adverse pregnancy outcomes in the GDM-group (p<0.05). Receiver operating characteristic (ROC) curve showed that the area under the curve (AUC) for diagnosing adverse pregnancy outcomes in pregnant women with GDM using serum GSP, Hcy, and CysC levels alone were 0.817, 0.843, and 0.775, respectively. The AUC of the three indicators combined was 0.921, indicating that this combination has a good predictive value for diagnosing adverse outcomes in GDM-complicated pregnancies.

Conclusions: GDM is associated with a high risk of adverse pregnancy outcomes. Levels of serum GSP, Hcy, and Cys-C are higher in patients with GDM. The higher the levels of GSP, Hcy, and Cys-C, the greater the risk of adverse pregnancy outcomes. Combining these three indicators can effectively predict maternal pregnancy outcomes.

doi: https://doi.org/10.12669/pjms.40.4.8988

How to cite this: Chen Y, Li C, Lei Y, Zhang F. Effects of glycated serum protein, homocysteine, and cystatin-C levels on pregnancy outcomes in patients with gestational diabetes mellitus. Pak J Med Sci. 2024;40(4):572-576. doi: https://doi.org/10.12669/pjms.40.4.8988

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
From Challenges to Solutions: Mapping the Landscape of Medical Professionalism and Ethics in Pakistan
Authors: Sattar Kamran , Jawaid Shaukat Ali
Published: February 2024

doi: https://doi.org/10.12669/pjms.40.4.9346

How to cite this: Sattar K, Jawaid SA. From Challenges to Solutions: Mapping the Landscape of Medical Professionalism and Ethics in Pakistan. Pak J Med Sci. 2024;40(4):557-558. doi: https://doi.org/10.12669/pjms.40.4.9346

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The influence of the ketogenic diet on the therapy of type 2 diabetes. A literature review
Authors: Madej Aleksandra , Senat Hanna , Grabowska Patrycja , Bolla Patrycja , Senat Aleksandra , Marczyńska Zuzanna , Dorochowicz Mateusz
Published: February 2024

INTRODUCTION: Diabetes mellitus type 2 (DM2) is a widespread and chronic disorder with high mortality and associated morbidity rates worldwide. DM2 individuals are more susceptible to developing heart disease, cardiovascular disease, diabetic neuropathy, and several other related complications, which are major causes of diabetes related death. New therapies and possibilities for the treatment of the diabetes are constantly being searched for, among which the ketogenic diet is increasingly becoming popular. There are studies being conducted all worldwide on the effects of this diet on the treatment of diabetes.

PURPOSE: The aim of the study is to present the current state of knowledge about the influence of the ketogenic diet on the therapy  of type 2 diabetes.

MATERIALS AND METHOD: The available literature in PubMed was reviewed to write the article, using the keywords ,,ketogenic diet”, ,,diabetes mellitus”, ,,ketogenic diet diabetes”, ,,diabetes nutrition”.  

CONCLUSION: A ketogenic diet has notable advantages on body weight and glycemic control, as well as on the enhancement of lipid profiles in overweight DM2 patients. This diet can decrease body weight, waist circumference, HbA1c, and triglycerides, and increase HDL levels. In addition, the ketogenic diet may have further benefits in improving body composition to attenuate the onset and progress of DM2 bz reducing body weight, lowering glycemic levels, and enhancing lipid profiles. More studies are needed in the future to support and even confirm the links between the ketogenic diet and patienst suffering from DM2.
Identification of High and Low Voltage Ride-Through Control Parameters for Electromechanical Transient Modeling of Photovoltaic Inverter
Authors: Jianjie Chen , Bo Zhao , Fang Zhang , Juan Hu , Li Zhang
Published: February 2024

The electromechanical transient model of a photovoltaic (PV) inverter’s high and low voltage ride-through has complex operating circumstances and a large number of control parameters, which makes parameter adjustment difficult. Furthermore, it is frequently challenging to identify a single set of control parameters that can successfully handle a variety of operating conditions. The Improved Differential Evolution Particle Swarm Optimization (IDEPSO) algorithm is proposed in this paper to provide a control parameter identification technique for high and low voltage ride-through that addresses these problems. Taking a 320kW PV inverter of a certain company as the research object, based on the specified current control strategy of high and low voltage ride-though, the parameters to be identified were determined by analyzing the influence of model parameters. Secondly, To enhance the algorithm’s capability to solve multidimensional optimization problems, convergence speed, and global search ability, the Differential Evolution (DE) algorithm’s search mechanism is incorporated into the Particle Swarm Optimization (PSO) algorithm, along with a non-fixed gradient inertia weight strategy for the algorithm’s inertia weight and the elite retention idea for the cross factor. Then, the objective function of the IDEPSO algorithm was built based on the concept of minimum deviation between simulation data and various groups of test data, and the significance of various working conditions was distinguished by weight division to improve the robustness of identification parameters. Finally, the identification parameters are imported into the PSASP program type 2 photovoltaic power station model, and the interval division and deviation calculation of the test data and simulation data are carried out. It is confirmed that the identification parameters meet the standards of the maximum variation permitted in GB/T 32892-2016 and are appropriate for a variety of working scenarios.
Optimal Design of PV/WT/Battery Based Microgrid for Rural Areas in Leh Using Dragonfly Algorithm
Authors: Yadav Subhash , Kumar Pradeep , Kumar Ashwani
Published: February 2024

This study proposes an optimal microgrid design for rural electrification in India’s Leh and Ladakh regions, using wind energy, solar, energy, and battery energy storage system. The Dragonfly Algorithm (DA) is used to calculate the optimal number of microgrid units, and results are compared with popular optimization algorithms such as Grey Wolf optimization (GWO), Differential Evolution (DE), and Discrete Harmony Search (DHS). The optimal design is based on an objective function to minimize the Levelized cost of energy (LCOE) while keeping the loss of power supply probability (LOPSP) as a reliability constraint. Three configuration studies are carried out, with three cases, each with a different maximum permissible LOPSP (LOPSPmax) value. The results show that optimal design and efficient energy management reliably meet the load demand. The energy generated from the proposed microgrid is clean compared to the grid supply, and the amount of greenhouse gas (GHG) emissions is reduced by 91.2% from Configuration-I, Case-I, which is the most economical configuration. The LCOE obtained from Configuration-I, Case-I is 0.129 $/kWh, the lowest among similar systems available in the literature. To determine the parameter cost with supply, the LCOE and Total life cycle cost (TLCC) sensitivity to LOPSPmax are considered. Furthermore, statistical analysis shows that DA outperforms GWO, DE, and DHS in terms of accuracy and convergence rate.
Optimization of the Fast Frequency Regulation Strategy for Energy Storage-Assisted Photovoltaic Power Stations
Authors: Zhou Jiayun
Published: February 2024

Photovoltaic (PV) power generation is characterized by randomness and intermittency, resulting in unpredictable fluctuations in output power. This presents a significant challenge to the stable operation of the grid. To address this issue, the integration of energy storage systems provides a solution to mitigate the volatility of PV output, ensuring stability and precise control. In this study, we propose an ASS-Elman-based equivalent droop control strategy for PV power stations participating in grid frequency regulation. Furthermore, a joint PV-energy storage frequency regulation system is developed. For energy storage power stations actively engaged in grid frequency regulation, we employ an adaptive droop control strategy to enhance the traditional droop control method by incorporating additional frequency control strategies. We validate the effectiveness of the proposed strategies through simulation using MATLAB/SIMULINK. The results demonstrate that these strategies maximize the potential of energy storage and PV systems to comprehensively regulate frequency and significantly improve primary frequency regulation performance.
Optimization on Photovoltaics and Energy Storage Integrated Flexible Direct Current Distribution Systems of Buildings Considering Load Uncertainty Using Scenario Generation Method
Authors: Wu Xiaorui , Chen Weidong , Tian Hao , Yao Zhiyang , Zhao Ergang , Guo Yongshui
Published: February 2024

This study explores the intricate challenge of energy demand uncertainty in the design of Photovoltaics and Energy Storage integrated Flexible Direct Current Distribution (PEDF) systems. Our objective is to examine the impact of different scenario generation methods on PEDF system optimization. We compare four approaches, including probabilistic techniques based on Monte Carlo simulation, Latin Hypercube Sampling for base scenario sampling, and a simulation-based method using building performance modeling. We evaluate these approaches using the Independent Scenario Optimization (ISO) and Two-Stage Stochastic Programming (TSSP) models, aiming to minimize the annual total cost within PEDF systems while addressing uncertainties. Our findings shed light on the optimal PEDF design under uncertainty, offering valuable insights for future decision-making in dynamic energy systems.
Resolution and Analysis of Transmission Line Fault Types Based on Recording Type Data and Deep Learning
Authors: Yang Qingbo , Zhang Kaiping , Yang Yingpo , Li Hongya , Sun Mengmeng
Published: February 2024

The reliable identification of fault types in transmission lines is essential for restoring power supply swiftly and minimizing economic losses during outages, thereby ensuring the safe and efficient functioning of the power system. This paper addresses the challenge of low recognition accuracy in existing transmission line fault diagnosis methods and presents a novel approach based on fault recording data collected from both ends of the line. This method distinguishes between lightning-strike and non-lightning-strike faults, utilizing a deep learning network architecture to analyze time-domain information from recorded data, using the initial and terminal waveforms as inputs. The proposed fault identification model integrates fault current phase mode transformation, Local Mean Decomposition (LMD) decomposition, and spectral entropy analysis, applying deep learning principles to enhance fault detection precision. This comprehensive approach enables the effective identification of various fault types on transmission lines. Extensive simulation tests were conducted using a sophisticated fault simulation model developed within simulation software to validate the proposed algorithm’s efficacy. The results demonstrate the algorithm’s high accuracy and efficiency in recognizing various fault types on transmission lines.
Detecting Spam E-mails with Content and Weight-based Binomial Logistic Model
Authors: Indu Richa , Dimri Sushil Chandra
Published: February 2024

Spam e-mails are continuously increasing and are a serious threats to a network and its users. Several efficient methods are available regarding this context, but still, it is evolving randomly. Considering this, the proposed approach addresses the problem of spam detection by combining traditional content-matching criteria with the modified version of the binomial logistic algorithm. The work generates seven categories for content-matching, which begins from three basic categories, namely: special words, adult content, and specific symbols and digits. The remaining four categories are derived from various possible combinations of these basic categories. The words selected for each category are carefully curated based on the human psychology of action and reaction. Then, a weight is assigned to each of the categories to signify their importance and a threshold criterion is deployed before implementing the binomial logistic algorithm, which not only increases the efficiency of the proposed algorithm but also reduces the rate of misclassification. The proposed model is tested on six separate datasets of Enron Spam Corpus, where 98.31% and 92.575% are the maximum and minimum accuracies achieved, respectively, in spam e-mail classification. The AUC_ROC scores for the entire Spam Corpus range between 0.927 and 0.983. A comparison is also carried out between the proposed algorithm and the other methods of spam detection that have logistic regression. Finally, the suggested method can adequately handle a large sample size without compromising the efficacy, which is measured using accuracy, precision, recall, F-measure, and AUC_ROC score.
Improved Design of Concurrent Synchronization System Controller Based on Petri Net
Authors: Yang Xu , Guo Shaocui , Xiang Dongming , Yang Yuxin , Chen Yijun
Published: February 2024

At present, the advancement of science and technology has contributed to the emergence of various network parallel environments, web service concurrency environments and massively parallel processors. In order to abstract some practical systems into concurrent system models, it is necessary to conduct model analysis, and to perform deadlock detection of the concurrent system. Despite prior studies on the control problem of the synchronous concurrent system, there remains room for improvement in terms of workload, time, and efficiency. Some very practical methods are explored by introducing locking and unlocking to synchronize the concurrent system, and the algorithm that involves the specific regulation is provided. The Petri net model of the controller is developed to provide an effective method applicable to find the deadlock and to prevent and eliminate the deadlock for synchronous concurrent systems.
Fine-grained Sentiment-enhanced Collaborative Filtering-based Hybrid Recommender System
Authors: Alatrash Rawaa , Priyadarshini Rojalina
Published: February 2024

Developing online educational platforms necessitates the incorporation of new intelligent procedures in order to improve long-term student experience. Presently, e-learning recommender systems rely on deep learning methods to recommend appropriate e-learning materials to the students based on their learner profiles. Fine-grained sentiment analysis (FSA) can be leveraged to enrich the recommender system. User-posted reviews and rating data are vital in accurately directing the student to the appropriate e-learning resources based on posted comments by comparable learners. In this work, a new e-learning recommendation system is proposed based on individualization and FSA. A hybrid framework is provided by integrating alternating least square (ALS) based collaborative filtering (CF) with FSA to generate an effective e-content recommendation named HCFSAR. ALS attempts to capture the learner’s latent factors based on their selections of interest to build the learner profile. Three FSA models based on attention mechanisms and bidirectional long short-term memory (bi-LSTM) are suggested and used to train twelve models in order to predict new ratings from learner-posted book reviews based on the extracted learner profile. HCFSAR used multiplication word embeddings for stronger corpus representation that were trained on a dataset generated for an educational context and showed a better accuracy of 93.39% for the best model entitled MHAM based ABHR-2 with multiplication (MHAAM), which performed better than other models. A tailored dataset that has been created by scraping reviews of different e-learning resources is leveraged to train different proposed models and validate against public datasets.
Prediction Model of Post-TAVR Complication Using a Medical Twin Web Navigator
Authors: Hyun Se-Min , Lee KangYoon
Published: February 2024

Transcatheter aortic valve replacement (TAVR) has been introduced as an alternative to surgical aortic valve replacement for patients with severe aortic valve disease and is expanding into a universal treatment. However, complications after TAVR can have devastating consequences for patients and must be predicted. By designing a TAVR medical twin architecture based on real-world data (RWD), we can minimize complications and achieve optimal clinical outcomes through analysis and simulation results in a virtual environment that can predict complications. The simulation phase utilizes machine learning algorithms for complication prediction to predict patients with conduction abnormalities, a complication of TAVR, and provides the prediction results through a web-based monitoring system. We also conduct research to identify factors that influence complications, so that complication prediction in a virtualized environment on a medical twin architecture can serve as a guide for personalized care design for patients undergoing TAVR.