bluuebunny/crossref_metadata_2025_split huggingface dataset:
E148Q mutation: a disease-causing mutation or a sequence variant? Disease severity in specific ethnic groups of Familial Mediterranean Fever patients
Authors: Awaad Elham Orouk , Straalen Joeri W , Miller-Barmak Adi , Gazitt Tal , Haddad-Haloun Jumana , Brik Riva , Saied Mohamad Hamad 
Published: March 2024

Abstract

Aim:
Evaluate clinical characteristics and disease severity in specific ethnic groups of FMF patients homozygous for the E148Q mutation, and examine the contribution of a single allele V726A mutation to disease severity.
Methods:
A retrospective cohort study included patients homozygous for the E148Q mutation. The Characteristics of FMF patients with E148Q/E148Q and E148Q/E148Q + V726A/- MEFV mutations were compared. The primary outcome measure was FMF disease severity as measured by Tel Hashomer Key to Severity Score among patients homozygous for the E148Q mutation alone or with an additional V726A mutation.
Results:
A total of 61 FMF patients were included of which 24 (39%) had the E148Q/E148Q MEFV mutation and 37 (61%) had the E148Q/E148Q + V726A/- MEFV mutation. Patients with the E148Q/E148Q + V726A/- MEVF mutation were significantly more often of Druze descent compared to patients with the E148Q/E148Q MEFV mutation. Most patients had mild or moderate disease severity. CRP levels decreased significantly after colchicine treatment.
Conclusion:
Patients with the E148Q mutation alone present with mild to moderate disease severity in all ethnic groups of Israeli FMF patients. Disease severity did not differ between patients with E148Q/E148Q + V726A/- MEFV mutation and E148Q/E148Q mutation alone. Colchicine treatment should be given
Indicators of arrhythmogenesis in dogs with Pulmonary Hypertension
Authors: Franco Luisa Fernanda Castillo , Silva Vinicius Bentivoglio , Vila Beatriz de Carvalho Pato , Endrighi Mariangela , Sousa Marlos Gonçalves 
Published: March 2024

Abstract
Pulmonary hypertension (PH) is a multifactorial syndrome that leads to cardiac muscle overload and distension. This generates right ventricular remodeling associated with sudden death (SCD) caused by ventricular arrhythmias. Electrocardiogram (ECG) is an important but simple tool for diagnosing arrhythmias allowing the early detection of SCD in ventricular remodeling due to PH through the measurement of the QT and T-peak T-end (TpTe) intervals which represent total ventricular electric activity and whose alteration may suggest a predisposition to SCD. This study aims to determine the role of arrhythmogenesis indicators in canine PH.Methods: Retrospective cardiologic evaluation of 53 dogs with PH (n=23 diagnosed with post-PH, and n=30 diagnosed with pre-PH), and 21 healthy (Control) dogs were used in this study. ECG records were used to determine, rhythm, heart rate, QRS duration, QRS dispersion, QT interval then corrected for heart rate (QTc); QT dispersion interval, QT interval variance (QTv), TpTe interval, and TpTe/QT. Prognostic and survival information were also recorded for the PH group. Results: QRS dispersion was significatively different (p 0.035) in dogs with PH compared with the healthy animals and when a cut-off value of 16.5 was applied there was a median survival time of 278 days in dogs with QRSdisp >16.5. QT, QTc, TpTe failed to demonstrate arrhythmogenic risk in dogs with PH. Conclusions: Dogs with post-PH showed a higher arrhythmogenic risk compared to animals with pre-PH and control animals. QRS dispersion may be used as an indicator of arrhythmogenesis in dogs with PH.
Enhanced Biometric Identification Systems: Deep Learning-Based Fingerprint Classification with VGG16 Model
Authors: Nahar Prateek 
Published: March 2024

Abstract

Biometric systems authenticate individuals based on unique physiological or behavioural traits for identification and access control, providing heightened security compared to traditional password-based methods. Automatic fingerprint recognition is widely employed for its reliability and uniqueness, yet fingerprint classification remains challenging in large-scale recognition systems due to variability, noise, and ambiguity. This paper presents a deep learning approach to fingerprint classification, utilizing the VGG16 model for feature extraction and classification. By employing precise algorithms, our method accelerates identification by reducing the number of comparisons needed during fingerprint retrieval. Our framework, tested on the FVC2004 DB1 database, demonstrates significant advancements in accuracy, highlighting deep learning's efficacy in enhancing biometric identification systems.
Impact of Key Performance Indicators on Radio Availability in Tnm's Mobile Network Across Malawi: a Regional Analysis
Authors: Luhanga Lawrence , Selvakumar Kanakaraj 
Published: March 2024

Abstract

This research examines the impact of Key Performance Indicators on Telekom Networks radio availability in several regions, including BT Urban, Rural South, Central, and North. Through exploratory data analysis, we prepared datasets for an in-depth examination. Given the complexity of linking Key Performance Indicators directly with radio availability, our method utilised scatter graphs to visualise and analyse regional and temporal variations in network performance. The study identifies substantial inconsistencies in radio availability, suggesting opportunities for network enhancements. Although no direct correlation between Key Performance Indicators’ and radio availability was found, the visual analysis highlights significant regional performance differences. It emphasizes the importance of ongoing network monitoring and specific improvements to boost Telekom Networks' radio availability. A key challenge was the indirect analysis approach necessitated by the varied dataset structures, pointing out the need for more comprehensive data that could facilitate direct correlations. This investigation enriches the understanding of Key Performance indicators' effects on radio availability and paves the way for future research on mobile network optimisation.
Genetic clines across the Atlantic-Mediterranean transition in the harbour crab Liocarcinus depurator
Authors: García-Merchán Victor Hugo , Palero Ferran , Rufino Marta , Macpherson Enrique , Abelló Pere , Pascual Marta 
Published: March 2024

Abstract

Environmental gradients in the sea may coincide with phenotypic or genetic gradients resulting from an evolutionary balance between selection and dispersal (i.e. marine clines). The population differentiation of the harbour crab, Liocarcinus depurator, an important by-catch species in the Mediterranean Sea and North-East Atlantic, was assessed here using both genetic and morphometric approaches. A total of 472 specimens were collected along its distribution area, and 17 morphometric landmarks, one mitochondrial gene (COI) and 11 polymorphic microsatellite markers were scored in 350, 287 and 280 individuals, respectively. Morphometric data lacked significant differences, but genetic analyses showed a reduction in gene flow between Atlantic and Mediterranean populations, with a steeper gradient in COI compared to microsatellite markers. Interestingly, nuclear differentiation was due to an outlier locus overlapping with the mtDNA genetic gradient. Such overlapping clines are likely to be maintained by natural selection along the Atlantic-Mediterranean transition area. Our results suggest a scenario of past isolation with local adaptation and secondary contact between the two basins. The process of vicariance may reinforce genetic differentiation at loci maintained by environmental selection even after secondary contact.
Beyond Text: The Impact of Clinical Context on GPT-4’s 12-lead ECG Interpretation Accuracy
Authors: Lisicic Ante , Jordan Ana , Serman Ana , Jurin Ivana , Novak Andrej , Pavlovic Nikola , Manola Sime , Zeljkovic Ivan 
Published: March 2024

Abstract
Introduction
Artificial intelligence (AI) and large language models (LLMs), such as OpenAI's Chat Generative Pre-trained Transformer – version 4 (GPT-4), are being increasingly explored for medical applications, including clinical decision support. The introduction of the capability to analyze graphical inputs marks a significant advancement in the functionality of GPT-4. Despite the promising potential of AI in enhancing diagnostic accuracy, the effectiveness of GPT-4 in interpreting complex 12-lead electrocardiograms (ECGs) remains to be assessed.
Methods
This study utilized GPT-4 to interpret 150 12-lead ECGs from the Cardiology Research Dubrava (CaRD) registry, spanning a wide range of cardiac pathologies. The ECGs were classified into four categories for analysis: Arrhythmias (Category 1), Conduction System abnormalities (Category 2), Acute Coronary Syndrome (Category 3), and Other (Category 4). Two experiments were conducted: one where GPT-4 interpreted ECGs without clinical context and another with added clinical scenarios. A panel of experienced cardiologists evaluated the accuracy of GPT-4's interpretations. Statistical significance was determined using the Shapiro-Wilk test for distribution, Mann-Whitney U test for continuous variables, and Chi-square/Fisher's exact tests for categorical variables.
Results
In this cross-sectional, observational study, GPT-4 demonstrated a correct interpretation rate of 19% without clinical context and a significantly improved rate of 45% with context (p < 0.001). The addition of clinical scenarios significantly enhanced interpretative accuracy, particularly in the Category 3 (Acute Coronary Syndrome) (10 vs. 70%, p < 0.0.01). Unlike Category 4 (Other) which showed no impact (51 vs. 59%, p = 0.640), an impact with a trend toward significance was observed in Category 1 (Arrhythmias) (9.7 vs. 32%, p = 0.059) and Category 2 (Conduction System abnormalities) (4.8 vs. 19%, p = 0.088) when tasked with context.
Conclusion
While GPT-4 shows some potential in aiding ECG interpretation, its effectiveness varies significantly depending on the presence of clinical context. The study suggests that, in its current form, GPT-4 alone may not suffice for accurate ECG interpretation across a broad spectrum of cardiac conditions.
Detection Methods of Hardware Trojans Based on DBN and QPSO-OCSVM Anomalous Behavior Analysis
Authors: ZHANG Lei , ZHANG Tian-Yu , XIAO Chao-En , WANG Jian-Xin , LIU Dongyang , DING Ding 
Published: March 2024

Abstract

At present, hardware trojan detection methods based on the side channel analysis technology mainly depend on the presence of the template chip, resulting in a problem that it is difficult to put the methods into practical applications. In addition, detection focus is always paid on the impact of the hardware trojan on the target circuit, and impact of hardware trojan trigger state on the target circuit is ignored. To address the abovementioned problems, a hardware trojan detection method based on anomalous behavior analysis is proposed in this paper. Firstly, detection theory based on anomalous behavior analysis is proposed, and an anomalous behavior analysis methods based on the DBN (Deep Belief Networks) and the QPSO-OCSVM (Quantum Particle Swarm Optimization-One Class Support Vector Machine) is designed, based on which a detection model is established.
This paper first proposed the detection theory based on anomalous behavior analysis, designs the anomalous behavior analysis methods based on DBN and QPSO-OCSVM, and build a detection model on this basis. Then, a hardware trojan detection platform designed and implemented, where three trigger-type hardware Trojans for the AES(Advanced Encryption Standard) encryption in the international Trust-Hub library are selected as the detection object and implanted to the encryption chip. Finally, the DBN model is utilized to extract the feature and reduce the dimensionality of the captured behavior data. The QPSO-OCSVM is used for detecting the anomalous behavior and obtaining the final detection result. Experimental results show that the detection method proposed in this paper could achieve the detection accuracy higher than 99% for the hardware trojans with resource occupancy rates of only 0.33%, 0.34%, and 0.76%. Optimal analysis model is established and a performance comparison with the anomalous detection model based on Isolation Forest and Local Outlier Factor is implemented, exhibiting that the detection model proposed in this paper has the highest performance.
A rare case of intestinal tuberculosis associated hypercalcaemia
Authors: Aponso Tilan , Wanninayaka W. S. , Nawarathna N. M.M. 
Published: March 2024

Abstract
Tuberculosis is a disease that can affect many organs in the human body and is caused by Mycobacterium tuberculosis. Abdominal tuberculosis is uncommon, with a reported incidence of 2.5% among extrapulmonary tuberculosis patients in the United States. Hypercalcemia in tuberculosis is a rare complication with a prevalence that varies between 2.3 and 28%. Most hypercalcemic patients with tuberculosis remain asymptomatic. (88%) We report a patient with non-PTH-related hypercalcemia as the presenting complaint who was later diagnosed with intestinal tuberculosis.
Comparison of contrast-enhanced mammography and breast magnetic resonance in the evaluation of response to neoadjuvant chemotherapy
Authors: Acar Omer , Ekinci Ferhat , Açar Çağdaş Rıza , Örgüç İhsan Şebnem , Şahbazlar Mustafa , Erdoğan Atike Pınar 
Published: March 2024

Abstract
Objective: Contrast-enhanced mammography (CEM) is a relatively inexpensive and convenient new imaging method obtained by administering enhanced iodinated contrast agent using the dual-energy technique to evaluate tumor neovascularity. CEM provides similar morphological and physiological information to breast magnetic resonance (MRI), the gold standard for detecting breast cancer. We aimed to compare CEM techniques, which have increased in popularity in recent years, with MRI, which has been the gold standard imaging technique to date, using the histopathological evaluation of patients with breast cancer who were operated after receiving neoadjuvant chemotherapy (NAC).
Material and Method: CEM and breast MRI images of patients who were scheduled for surgery due to locally advanced breast cancer were taken before and after NAC. Responses to treatment and residual tumor sizes of patients operated after NAC were recorded.
Results: CEM estimated the residual tumor size to within 1 cm accuracy in 84.7% of the patients. MRI estimated the residual tumor size to within 1 cm accuracy in 76.4% of the patients. When we used histopathological evaluation as a reference, the Lin’s correlation concordance was higher for CEM than MRI. The sensitivity, NPV and PPV were higher compared to MRI. The superiority of MRI over CEM was its higher specificity rate.
Conclusion: Our findings showed that in patients receiving NAC, CEM was superior to MRI in identifying patients with pathological complete response and was at least as successful as MRI in patients with residual tumor.
QuATON: Quantization Aware Training of Optical Neurons
Authors: Wadduwage Dushan , Kariyawasam Hasindu , Hettiarachchi Ramith , Yang Quansan , Matlock Alex , Nambara Takahiro , Kusaka Hiroyuki , Kunai Yuichiro , So Peter , Boyden Edward 
Published: March 2024

Abstract

Optical processors, built with "optical neurons", can efficiently perform high-dimensional linear operations at the speed of light. Thus they are a promising avenue to accelerate large-scale linear computations. With the current advances in micro-fabrication, such optical processors can now be 3D fabricated, but with a limited precision. This limitation translates to quantization of learnable parameters in optical neurons, and should be handled during the design of the optical processor in order to avoid a model mismatch. Specifically, optical neurons should be trained or designed within the physical-constraints at a predefined quantized precision level. To address this critical issues we propose a physics-informed quantization-aware training framework. Our approach accounts for physical constraints during the training process, leading to robust designs. We demonstrate that our approach can design state of the art optical processors using diffractive networks for multiple physics based tasks despite quantized learnable parameters. We thus lay the foundation upon which improved optical processors may be 3D fabricated in the future.
Mutant p53 Induces Amyloid Aggregation in p63 and p73 Liquid Droplets: Mechanistic Insights into Oncogenic Phase Transitions
Authors: Silva Jerson , Petronilho Elaine , de Andrade Guilherme , de Sousa Gileno , Almeida Fernando , Mota Michelle , Marques Mayra , Vieira Tuane , de Oliveira Guilherme 
Published: March 2024

Abstract
Phase separation (PS) of p53 is critical in its path to amyloid aggregation, a process linked to cancer. P63 and p73 exhibit dual roles as tumor suppressors and oncogenes and are often heightened in tumors. Their coaggregation has been proposed in cancer, yet their PS contribution remains unknown. This study investigates the phase behaviors of p53, p63, and p73. P63 and p73 undergo liquid-liquid phase separation (LLPS). Unlike p53, p63 and p73 do not form amyloids under increased temperatures, underscoring an aspect of the processes involved in cancer. Unlike p63 and p73, wild-type and the M237I mutant p53 initially form droplets at 4°C, but at temperatures up to 37°C, they begin to aggregate and bind to Congo red, showing amyloidogenesis. Intriguingly, mutant p53 promotes amyloid-like states in p63 and p73 and hijacks p73 into membrane less organelles. Wild-type p53 has a moderate effect on p63 and p73's amyloid aggregation. Additionally, heparin prevents the prion-like aggregation of p63 and p73 induced by p53. Our results shed light on how mutant and wild-type p53 may trigger amyloid aggregation of p63 and p73, revealing the capacity of p53 amyloid droplets within cancer. These insights expand the possibilities for developing cancer therapies targeting the prion-like conversion of p63 and p73 influenced by mutated p53.
A multicenter analysis of registry data on postoperative orthopedic pain: a retrospective cohort study
Authors: Thiel Bram , Godfried Marc B. , Koopman Seppe J.H.A. , Huijboom Marina , Opschoor Krijna , Aarnoudse Martijn , Poolman Rudolf W. , Verlaan Jorrit Jan , Kitzen Joep , Kalkman Cor J. 
Published: March 2024

Abstract
Background
Postoperative pain has significant implications, including surgical complications, prolonged hospital stays, and increased healthcare costs. The severity of postoperative pain depends predominantly on the surgical procedure, with orthopedic surgeries posing an elevated risk for severe pain. This study aimed to identify orthopedic surgical procedures that induce severe pain in hospitalized patients within the first 72 hours after surgery.
Methods
We retrospectively analysed nurse-recorded pain scores from patients who received inpatient orthopaedic surgery between October 15, 2015, and December 31, 2019, in three different hospitals. Patient and procedure characteristics and the highest numeric rating scale for pain were collected up to 72 hours after surgery. Procedures were coded according to the International Classification of Health Interventions and subsequently ranked based on the highest median numerical rating scale for pain.
Results
A total of 9.918 patients were included in the analysis. The median highest pain score between 3 and 24 hours and between 24 and 48 hours postoperatively was 4 (IQR 3). The median highest pain score between 48 and 72 hours postoperatively was 3 (IQR 2). Open reduction and implantation of the device into the humerus (e.g., plate fixation) ranked highest, with a median NRS for pain of 6 (IQR 3) in the 3- to 48-hour postoperative period and a median NRS of 5 (IQR 3) in the 48- to 72-hour postoperative period. Open procedures of the shoulder joint, open ankle procedures and open fusion of the lumbar spine were among the top ten procedures with the highest median pain scores.
Conclusions
Patients who underwent surgery involving the humerus, shoulder, or lumbar spine had greater pain scores. Further analysis, including the inclusion of analgesic medication data, can contribute to a better understanding of effective interventions for managing postoperative pain for these specific procedures.
Trial registration
The study received formal ethical approval from the ethical committee and the institutional research review board of OLVG Hospital on April 25, 2017, with reference number ACWO-MEC 17u.207/WCHJ/WO 17.051.
Risk Mapping of Groundwater Level in Peatland Area Utilizing a Spatio- Temporal Model with Weight Constructed Based on Minimum Spanning Tree
Authors: Mukhaiyar Utriweni , Mahdiyasa Adilan Widyawan , Suherlan Bagas Caesar , Pasaribu Udjianna Sekteria Pasaribu , Sari Kurnia Novita , Viridi Sparisoma , Indratno Sapto Wahyu , Humam Afif 
Published: March 2024

Abstract
Space-time extrapolation models are usually constrained to a limited number of observed locations and lack the ability to provide information about the values at unobserved locations. However, integrating these models with spatial interpolation techniques, it is possible to obtain more informative visual representations. The Generalized Space-Time Autoregressive (GSTAR) model, as a multivariate space-time extrapolation model, is often used due to its simplicity. Within the framework of the GSTAR model, a crucial component is the spatial weight matrix, which facilitates the establishment of spatial relationships among different locations. This matrix can be constructed by employing graph theory, particularly Minimum Spanning Tree (MST), as an extension of the model. Additionally, spatial interpolation can be achieved through the utilization of kriging methods, by gridding the observed spatial locations. Although the amalgamation of these two models does not exhibit superior performance compared to univariate time series models in risk mapping, particularly in the context of groundwater level observed in peatland areas within Riau Province, Indonesia, the model can provide more robust conclusions.