Hasil untuk "Diseases of the genitourinary system. Urology"

Aurora Hirvonen, S. Comero, Simona Tavazzi et al.

The Encyclopaedia Cloacae is a novel and centralised digital platform designed to support and advance wastewater-based epidemiology (WBE) by cataloguing pathogens detectable in wastewater and their relevance to public health surveillance. The platform is hosted on the EU Wastewater Observatory for Public Health (EU4S) website, where it is populated with peer-reviewed research through a structured workflow under harmonised criteria which address the presence of pathogens in human excreta, detectability in wastewater, and integration into public health systems. This tri-criteria approach ensures that the database is both scientifically robust and operationally actionable. Complemented by the Visualising the Invisible dashboard, the platform offers geospatial insights into global WBE research activity. By consolidating peer-reviewed evidence on pathogen detectability in wastewater and human excreta, the Encyclopaedia Cloacae enables early detection of infectious diseases, whether already known or newly emerging. The continuously updated repository and geospatial dashboards help to identify surveillance gaps and research hotspots, to support timely public health responses, enhance pandemic preparedness, and strengthen global health security. In addition, it supports One Health strategies, connecting the health of humans, animals, and the shared environment. This article outlines the platform’s architecture, data curation methodology, and future directions, including automation and expansion to encompass broader health determinants such as antimicrobial resistance and chemical hazards.

A. Faragalli, R. Franceschi, M. Marigliano et al.

Introduction Achieving and maintaining optimal glycemic control from the onset of type 1 diabetes (T1D) is crucial in pediatric care, especially in early childhood when the developing brain is highly vulnerable to both hypo- and hyperglycemia [1-3]. Hyperglycemia during early childhood increases the risk of long-term vascular complications, while severe hypoglycemia may impair neurocognitive development, causes family anxiety, and complicates social integration [4-5]. Although automated insulin delivery (AID) systems have demonstrated efficacy in controlled trials, real-world evidence in children under six years of age, particularly involving off-label use, remains limited. The Control-IQ (CIQ) algorithm, integrated into the Tandem t:slim X2 insulin pump, has shown benefits in adolescents and school-aged children [6-10]. However, few studies have evaluated its long-term use in children under six in routine clinical practice. Objective This study aimed to compare the real-world effectiveness and safety of the CIQ system in two pediatric age groups—children aged 0.5–5 years and children aged 6–10 years—over an 18-month follow-up period. We evaluated effectiveness in terms of glycemic control (% of time in glucose range 70–180 mg/dL [TIR], % of time in glucose range 70–140 mg/dL [TITR], and HbA1c) and safety in term of adverse events (diabetic ketoacidosis [DKA], hyperglycemia and severe hypoglycemia). Methods This prospective, multicenter observational study used retrospective data from 32 Italian pediatric diabetes centers. Eligible participants had T1D diagnosed  ≥ 6 months, , were 10 years at CIQ start were excluded. Participants were stratified by age at CIQ initiation (0.5–5 and 6–10 years). At CIQ initiation (baseline) sex, presence of celiac disease or thyroiditis and parents’ age, nationality and education, were collected. HbA1c, BMI z-score, CGM-derived data (TIR, TITR, % of time spent in glucose ranges: 250 mg/dL, Glucose Monitoring Indicators and coefficient of variation of glucose), Glycemia, Standard Deviation of Glycemia [SD] and DKA episodes were assessed at baseline, 6, 12, and 18 months. Descriptive statistics were used for baseline comparisons. Chi-square or t tests evaluated group differences. Trend over time points in TIR, TITR, and HbA1c were analysed using mixed-effects models for repeated measures, adjusted by age group, sex, time from diagnosis to CIQ initiation, DKA at onset and parents’ socio-economic characteristics (at least one non-Italian parent, parents’ education). A sequential difference contrast was used to model time; interaction between time and age groups was evaluated. Only children with complete data on the outcomes at all four time points were included in these models. Safety outcomes included the proportions of DKA and severe hypoglycaemias occurring during 18-month follow-up. Results Of the 334 children enrolled, 253 (106 aged 0.5–5; 147 aged 6–10) had complete data on the outcomes and were included in longitudinal analyses. At T1D diagnosis, a higher prevalence of thyroiditis in the older group was found, and no significant sociodemographic differences. At CIQ initiation, younger children had a significantly shorter time from diagnosis to CIQ initiation (1.36 vs 2.61 years, p<0.001), higher HbA1c (8.3%% vs 7.7%, p=0.020) and higher glycaemic variability (SD 63.3 mg/dL vs 58.3 mg/dL, p = 0.023) while TIR, TITR, and the other CGM-derived data were comparable. Longitudinal analysis (Figure 1) showed significant improvement in both groups 6 months after CIQ initiation: TIR increased by 6.62% (95% CI: 4.89–8.36) and TITR by 5.63% (95% CI: 3.61–7.66), corresponding to over 80 additional minutes/day spent in target ranges. These improvements were sustained at 12 and 18 months. HbA1c decreased by an average of 0.82% (95% CI: –1.01 to –0.62) in the first 6 months, remaining stable thereafter. No significant interaction between time and age groups was observed, indicating similar trends in both cohorts. Having at least one non-Italian parent was significantly associated with lower TIR (-5.82%, 95% CI: -10.33 to -1.31) and higher HbA1c levels (0.31%, 95% CI: 0.01 to 0.63). A high parental education level (university or higher vs. up to lower secondary education) was associated with higher TIR (8.61%, 95% CI: 3.03–14.18) and lower HbA1c levels (−0.42%, 95% CI: −0.78 to −0.06). Age at CIQ initiation, time from diagnosis to CIQ initiation, DKA at diagnosis, and sex were not significant predictor. Regarding safety, no severe hypoglycaemia episodes were reported in the younger group, and only one occurred in the older group after 12 months. A single DKA episode was recorded in a child under six. Moreover, CGM-derived data indicated that time spent in hypoglycaemia (<54 and 54–69 mg/dL) remained consistently below clinically relevant thresholds (<1% and <3%, respectively). Conclusion In this large real-world cohort of young children with T1D, the CIQ system demonstrated consistent and sustained improvements in glycaemic outcomes over 18 months, with minimal adverse events. Significant gains in TIR, TITR, and HbA1c were observed in both age groups, particularly in the first 6 months after CIQ initiation. These benefits were maintained long-term, regardless of initial glycaemic status and presence of DKA at diagnosis. The system proved safe even in children under six, supporting its current use in off-label settings with appropriate clinical oversight. Our findings reinforce the value of early AID adoption to optimize long-term metabolic outcomes in pediatric T1D.

James M Jones, D. Barham, Martin S. Gross et al.

Anthony J. Webster

There is a growing proportion of people with several disease conditions ("multimorbidity"), placing increasing demands on healthcare systems. One hypothesis is that clusters of diseases may arise from shared underlying disease processes (shared "pathogenesis"), whereby the presence of one disease indicates the state of disease progression to several related disease types. This article explains how this hypothesis can be tested using observational data for disease incidence. Specifically, a multistage model is used to test whether two diseases can have a "shared stage" or "step", before either disease can occur, and how the unobserved rate of this step can be determined. The approach offers a simple method for studying multiple diseases and identifying shared underlying causes of multiple conditions, and is illustrated with published data and numerical examples. The fundamental mathematical model is analysed to compare key statistical properties such as the expectation and variance with those of independent diseases. The main results do not need an understanding of the underlying mathematics and can be appreciated by a non-expert. Significance: It is widely believed that there are shared underlying pathways that can lead to several disease types (shared "pathogenesis"), and this may explain observed clusters of disease types. This article shows how this hypothesis can be tested for a pair or cluster of diseases, using observational data of disease incidence.

G. Nketiah, Mohammed R S Sunoqrot, E. Sandsmark et al.

Objective: To develop and evaluate a deep radiomics model for clinically significant prostate cancer (csPCa, grade group>= 2) detection and compare its performance to Prostate Imaging Reporting and Data System (PI-RADS) assessment in a multicenter cohort. Materials and Methods: This retrospective study analyzed biparametric (T2W and DW) prostate MRI sequences of 615 patients (mean age, 63.1 +/- 7 years) from four datasets acquired between 2010 and 2020: PROSTATEx challenge, Prostate158 challenge, PCaMAP trial, and an in-house (NTNU/St. Olavs Hospital) dataset. With expert annotations as ground truth, a deep radiomics model was trained, including nnU-Net segmentation of the prostate gland, voxel-wise radiomic feature extraction, extreme gradient boost classification, and post-processing of tumor probability maps into csPCa detection maps. Training involved 5-fold cross-validation using the PROSTATEx (n=199), Prostate158 (n=138), and PCaMAP (n=78) datasets, and testing on the in-house (n=200) dataset. Patient- and lesion-level performance were compared to PI-RADS using area under ROC curve (AUROC [95% CI]), sensitivity, and specificity analysis. Results: On the test data, the radiologist achieved a patient-level AUROC of 0.94 [0.91-0.98] with 94% (75/80) sensitivity and 77% (92/120) specificity at PI-RADS>= 3. The deep radiomics model at a tumor probability cut-off>= 0.76 achieved 0.91 [0.86-0.95] AUROC with 90% (72/80) sensitivity and 73% (87/120) specificity, not significantly different (p = 0.068) from PI-RADS. On the lesion level, PI-RADS cut-off>= 3 had 84% (91/108) sensitivity at 0.2 (40/200) false positives per patient, while deep radiomics attained 68% (73/108) sensitivity at the same false positive rate. Conclusion: Deep radiomics machine learning model achieved comparable performance to PI-RADS assessment in csPCa detection at the patient-level but not at the lesion-level.

MAIKOALEJANDRO MAIKO ALEJANDRO, ANNIA AGUILAR LOAYZA, JUAN FERNANDOMAMANI OCHOA et al.

Aldo Renato Samayoa Ramírez

La anatomía humana ha sido objeto de estudio y descripción a lo largo de la historia, y la terminología utilizada para referirse a las estructuras del cuerpo ha evolucionado con el tiempo. Desde inicios del siglo XX se formó la Federación Internacional de Asociaciones de Anatomistas (IFAA del inglés International Federation of Associations of Anatomists) reuniendo asociaciones de todo el mundo, estudiando la anatomía humana y las ciencias biomorfológicas.1

Zaisheng Zhu, Yiyi Zhu, Hongqi Shi et al.

ObjectiveThis study aims to investigate and analyze the feasibility, oncological outcomes, functional efficacy, and complications with the prostatic capsule sparing (PCS) as well as the nerve sparing (NS) in radical cystectomy for bladder cancer.Patients and methodsBetween January 2007 and December 2021, 67 total cystectomies with PCS and 54 with NS were performed at our institution. The inclusion criteria for PCS were as follows: proactive, fully informed patient consent; negative transurethral resection of the bladder neck; normal prostate-specific antigen (PSA) level &lt; 4 ng/dL; and normal transrectal ultrasonography with biopsy of any suspicious nodes. Patients received complete oncological and functional follow-ups. The Kaplan-Meier method was utilized to characterize survival outcomes after surgery.ResultsThe median follow-up times for PCS and NS were 144 and 122 months, respectively. Cumulative survival estimated the 5- and 10-years cancer-specific survival were 93.0% and 88.7% for the PCS group and 79.7% and 79.6% for the NS group, respectively (p = 0.123). In terms of function, the daytime urinary control at 3, 6, and 12 months postoperatively was 80.60%, 97.01%, and 100% in the PCS group, and 53.70%, 85.19%, and 94.44% in the NS group, respectively (p = 0.002, 0.023, and 0.100); and nocturnal urinary control was 62.69%, 94.03%, and 98.51% in the PCS group, and 40.74%, 72.22%, and 87.04% in the NS group, respectively (p = 0.016, 0.001, and 0.022). The erectile function recovery revealed that 62.69% and 40.74% of patients returned to preoperative levels (International Index of Erectile Function (IIEF)-5 score ≥ 15) in the PCS and NS groups, respectively (p = 0.016). Considering complications within 30 days after surgery, 4.48% and 7.69% patients had Clavien ≥ III complications in the PCS and NS groups, respectively (p = 0.700).ConclusionThe PCS provides better restored urinary control and sexual function than the NS technique and does not affect oncological outcomes. However, PCS is prone to bladder-neck obstruction complications and requires closer long-term follow-up.

Zihan Wang, Daniel W. Yang, Zerui Liu et al.

This study presents the first implementation of multilayer neural networks on a memristor/CMOS integrated system on chip (SoC) to simultaneously detect multiple diseases. To overcome limitations in medical data, generative AI techniques are used to enhance the dataset, improving the classifier's robustness and diversity. The system achieves notable performance with low latency, high accuracy (91.82%), and energy efficiency, facilitated by end-to-end execution on a memristor-based SoC with ten 256x256 crossbar arrays and an integrated on-chip processor. This research showcases the transformative potential of memristive in-memory computing hardware in accelerating machine learning applications for medical diagnostics.

Shams Nafisa Ali, Afia Zahin, Samiul Based Shuvo et al.

Cardiac auscultation, an integral tool in diagnosing cardiovascular diseases (CVDs), often relies on the subjective interpretation of clinicians, presenting a limitation in consistency and accuracy. Addressing this, we introduce the BUET Multi-disease Heart Sound (BMD-HS) dataset - a comprehensive and meticulously curated collection of heart sound recordings. This dataset, encompassing 864 recordings across five distinct classes of common heart sounds, represents a broad spectrum of valvular heart diseases, with a focus on diagnostically challenging cases. The standout feature of the BMD-HS dataset is its innovative multi-label annotation system, which captures a diverse range of diseases and unique disease states. This system significantly enhances the dataset's utility for developing advanced machine learning models in automated heart sound classification and diagnosis. By bridging the gap between traditional auscultation practices and contemporary data-driven diagnostic methods, the BMD-HS dataset is poised to revolutionize CVD diagnosis and management, providing an invaluable resource for the advancement of cardiac health research. The dataset is publicly available at this link: https://github.com/mHealthBuet/BMD-HS-Dataset.

Lang Cao, Jimeng Sun, Adam Cross

Rare diseases affect millions worldwide but often face limited research focus due to their low prevalence. This results in prolonged diagnoses and a lack of approved therapies. Recent advancements in Large Language Models (LLMs) have shown promise in automating the extraction of medical information, offering potential to improve medical diagnosis and management. However, most LLMs lack professional medical knowledge, especially concerning rare diseases, and struggle to handle the latest rare disease information. They also cannot effectively manage rare disease data and are not directly suitable for diagnosis and management tasks. Our objective is to create an end-to-end system called AutoRD, which automates the extraction of information from medical texts about rare diseases, focusing on entities and their relations. AutoRD integrates up-to-date structured knowledge and demonstrates superior performance in rare disease extraction tasks. We conduct various experiments to evaluate AutoRD's performance, aiming to surpass common LLMs and traditional methods.

Bimarsha Khanal, Paras Poudel, Anish Chapagai et al.

Plant diseases significantly impact our food supply, causing problems for farmers, economies reliant on agriculture, and global food security. Accurate and timely plant disease diagnosis is crucial for effective treatment and minimizing yield losses. Despite advancements in agricultural technology, a precise and early diagnosis remains a challenge, especially in underdeveloped regions where agriculture is crucial and agricultural experts are scarce. However, adopting Deep Learning applications can assist in accurately identifying diseases without needing plant pathologists. In this study, the effectiveness of various computer vision models for detecting paddy diseases is evaluated and proposed the best deep learning-based disease detection system. Both classification and detection using the Paddy Doctor dataset, which contains over 20,000 annotated images of paddy leaves for disease diagnosis are tested and evaluated. For detection, we utilized the YOLOv8 model-based model were used for paddy disease detection and CNN models and the Vision Transformer were used for disease classification. The average mAP50 of 69% for detection tasks was achieved and the Vision Transformer classification accuracy was 99.38%. It was found that detection models are effective at identifying multiple diseases simultaneously with less computing power, whereas classification models, though computationally expensive, exhibit better performance for classifying single diseases. Additionally, a mobile application was developed to enable farmers to identify paddy diseases instantly. Experiments with the app showed encouraging results in utilizing the trained models for both disease classification and treatment guidance.

A. Maloberti, C. Giannattasio, M. Bombelli et al.

Benjamin Musheyev, M. Boparai, Reona Kimura et al.

Medical specialty usage of COVID-19 survivors after hospital discharge is poorly understood. This study investigated medical specialty usage at 1–12 and 13–24 months post-hospital discharge in critically ill and non-critically ill COVID-19 survivors. This retrospective study followed ICU ( N  = 89) and non-ICU ( N  = 205) COVID-19 survivors who returned for follow-up within the Stony Brook Health System post-hospital discharge. Follow-up data including survival, hospital readmission, ongoing symptoms, medical specialty care use, and ICU status were examined 1–12 and 13–24 months after COVID-19 discharge. “New” (not previously seen) medical specialty usage was also identified. Essentially all (98%) patients survived. Hospital readmission was 34%, but functional status scores at discharge were not associated with hospital readmission. Many patients reported ongoing [neuromuscular (50%) respiratory (39%), chronic fatigue (35%), cardiovascular (30%), gastrointestinal (28%), neurocognitive (22%), genitourinary (22%), and mood-related (13%)] symptoms at least once 1–24 months after discharge. Common specialty follow-ups included cardiology (25%), vascular medicine (17%), urology (17%), neurology (16%), and pulmonology (14%), with some associated with pre-existing comorbidities and with COVID-19. Common new specialty visits were vascular medicine (11%), pulmonology (11%), and neurology (9%). ICU patients had more symptoms and follow-ups compared to the non-ICU patients. This study reported high incidence of persistent symptoms and medical specialty care needs in hospitalized COVID-19 survivors 1–24 months post-discharge. Some specialty care needs were COVID-19 related or exacerbated by COVID-19 disease while others were associated with pre-existing medical conditions. Longer follow-up studies of COVID-19 survivor medical care needs are necessary.

Deroma, the Emilia-Romagna, Emilia-Romagna et al.

Abstract Background Heart failure (HF) affected 64.3 million people worldwide and contributed to 9.9 million years lived with disability globally in 2017. Despite its global relevance, there is a lack of comprehensive statistics on the prevalence, incidence, and burden of HF in developing Central Asian countries. This study aims to fill the gap and present the data for Kazakhstan, the largest Central Asian country. Methods HF cases were identified through the Unified National Electronic Healthcare System records for 2014-2021 using the appropriate ICD-10 codes. Descriptive and survival analyses were used to present demographics, incidence, prevalence, and mortality rates. The calculation of DALYs is done according to the WHO methods. The information on comorbid conditions based on respective ICD-10 codes was collected by merging the databases using unique deidentifying patient numbers. Results During the observation period between 2014-2021 years, 501,663 patients with HF were identified, of them 52% were females, 86% were older than 50 years of age, and 58% were of Kazakh ethnicity. Hypertension, history of cerebrovascular diseases, and myocardial infarction were present in 40%, 34%, and 22% of the cohort, respectively. The age and sex-specific incidence show that women have higher incidence before 30 years of age compared to men. In addition, incidence rates for both sexes and all age categories decreased in 2021 compared to 2014. The prevalence dramatically increased from 4393 people per million population (PMP) to 22,088 PMP, while mortality rates changed from 367 to 721 PMP during the observation period. In 2021, 2,964,062 age and disability-adjusted life years (DALYs) were lost due to HF in Kazakhstan. More than 2 million DALYs belong to years of life lost (YLLs). Conclusions The DALYs show high economic and social loss due to high mortality among patients. Healthcare policymakers should prioritize the enhancement of cardiac services and the mitigation of its risk factors. Key messages • This study highlights the high burden of heart failure in Kazakhstan, with over 2 million DALYs lost in 2021 alone. Healthcare policymakers must prioritize cardiac services and risk factor reduction. • Lack of comprehensive data on HF in Central Asia made it difficult to tackle the burden. This study fills this gap, presenting valuable information for policymakers to reduce social and economic loss.

Yi-Kang Sun, Yang Yu, Guang Xu et al.

The purpose of this study was to analyze the value of transrectal shear-wave elastography (SWE) in combination with multivariable tools for predicting adverse pathological features before radical prostatectomy (RP). Preoperative clinicopathological variables, multiparametric magnetic resonance imaging (mp-MRI) manifestations, and the maximum elastic value of the prostate (Emax) on SWE were retrospectively collected. The accuracy of SWE for predicting adverse pathological features was evaluated based on postoperative pathology, and parameters with statistical significance were selected. The diagnostic performance of various models, including preoperative clinicopathological variables (model 1), preoperative clinicopathological variables + mp-MRI (model 2), and preoperative clinicopathological variables + mp-MRI + SWE (model 3), was evaluated with area under the receiver operator characteristic curve (AUC) analysis. Emax was significantly higher in prostate cancer with extracapsular extension (ECE) or seminal vesicle invasion (SVI) with both P < 0.001. The optimal cutoff Emax values for ECE and SVI were 60.45 kPa and 81.55 kPa, respectively. Inclusion of mp-MRI and SWE improved discrimination by clinical models for ECE (model 2 vs model 1, P = 0.031; model 3 vs model 1, P = 0.002; model 3 vs model 2, P = 0.018) and SVI (model 2 vs model 1, P = 0.147; model 3 vs model 1, P = 0.037; model 3 vs model 2, P = 0.134). SWE is valuable for identifying patients at high risk of adverse pathology.

F Gabrigna Berto, J McClure, J Campbell et al.

A. Dominguez-bali, Z. Hassan, P. Shah et al.

Hormone Replacement Therapy (HRT) use has recently been a topic of debate amongst clinicians. The discussion has been most prevalent in the fields of gynecology, urology, and endocrinology. Current research suggests HRT can prevent the development of lethal diseases such as myocardial infarction, osteoporosis, diabetes mellitus, Alzheimer's disease, Parkinson’s disease, and genitourinary syndrome of menopause. To raise awareness of HRT as a prophylactic measure to treat a variety of diseases in multiple organ systems, we recommend providing this information across multiple medical specialties to warrant the use of HRT amongst patients. Specialists including obstetricians and gynecologists, urologists, cardiologists, neurologists, dermatologists, psychiatrists, ophthalmologists, orthopedics, wound care specialists, sexologists, sex therapists, psychologists, rheumatologists, immunologists, infectious disease specialists, cosmetic surgeons, endocrinologists, and geriatricians should be aware of the benefits of HRT and how they can incorporate it into patient care. Based on our own experience, for more than 40 years and the continued management of postmenopausal women, we have realized the importance that physicians, in specialties other than obstetrics and gynecology must be aware of the use of this therapy in their specific field. For example, approximately 60% of depression diagnoses in perimenopausal years can be treated with greater efficacy with hormone replacement therapy rather than antidepressants prescribed by psychiatrists. These findings have been confirmed in the current literature, however awareness amongst the various specialties is very poor. HRT plays a significant role in treating silent killers such as myocardial infarction, osteoporosis, diabetes mellitus, Alzheimer’s disease, Parkinson’s disease, and Genitourinary syndrome of menopause. Previously, the role of HRT was not clearly established. Current data suggests HRT provides many benefits including a reduction in mortality. Through recent research and published literature, the consensus of the benefits and strategies of HRT have been established. However, the use of HRT is currently under-prescribed. This creates an opportunity to bring awareness to clinicians, thereby improving the lives of patients. No

Aashish Cheruvu

While deep-learning based recommender systems utilizing collaborative filtering have been commonly used for recommendation in other domains, their application in the medical domain have been limited. In addition to modeling user-item interactions, we show that deep-learning based recommender systems can be used to model subject-disease code interactions. Two novel applications of deep learning-based recommender systems using Neural Collaborative Filtering (NCF) and Deep Hybrid Filtering (DHF) were utilized for disease diagnosis based on known past patient comorbidities. Two datasets, one incorporating all subject-disease code pairs present in the MIMIC-III database, and the other incorporating the top 50 most commonly occurring diseases, were used for prediction. Accuracy and Hit Ratio@10 were utilized as metrics to estimate model performance. The performance of the NCF model making use of the reduced "top 50" ICD-9 code dataset was found to be lower (accuracy of ~80% and hit ratio@10 of 35%) as compared to the performance of the NCF model trained on all ICD-9 codes (accuracy of ~90% and hit ratio@10 of ~80%). Reasons for the superior performance of the sparser dataset with all ICD codes can be mainly attributed to the higher volume of data and the robustness of deep-learning based recommender systems with modeling sparse data. Additionally, results from the DHF models reflect better performance than the NCF models, with a better accuracy of 94.4% and hit ratio@10 of 85.36%, reflecting the importance of the incorporation of clinical note information. Additionally, compared to literature reports utilizing primarily natural language processing-based predictions for the task of ICD-9 code co-occurrence, the novel deep learning-based recommender systems approach performed better. Overall, the deep learning-based recommender systems have shown promise in predicting disease comorbidity.

Mengjie Du, Xiang Fang, Jie Li

This technical report describes ChinaTelecom system for Track 1 (closed) of the VoxCeleb2023 Speaker Recognition Challenge (VoxSRC 2023). Our system consists of several ResNet variants trained only on VoxCeleb2, which were fused for better performance later. Score calibration was also applied for each variant and the fused system. The final submission achieved minDCF of 0.1066 and EER of 1.980%.