Medical History Research Articles

Gender-driven thyroid dysfunction in systemic lupus erythematosus patients: Clinical significance of an overlooked association and global stance

Aim of the workThis study aimed to determine the association of thyroid dysfunction in systemic lupus erythematosus (SLE) patients Patients and methodsOut of a cohort including 312 adult SLE patients those with thyroid dysfunction (n = 53) were further evaluated. The medical history, clinical examination, laboratory investigations and medications received were recorded. The SLICC damage index (SLICC-DI) was assessed. ResultsThyroid dysfunction formed 17 % of the patients and their mean age was 39.5 ± 11.5 years and disease duration 5.6 ± 3.3 years were comparable to those without thyroid disorder, however female gender was predominantly higher (F:M 52:1 vs 6.2:1, p < 0.0001). The frequency of thyroid dysfunction was alike in Saudi (16 %) and non-Saudi (17.9 %) patients (p = 0.66). The majority of cases (96.2 %) had hypothyroidism, one with autoimmune thyroiditis, while only 2 (3.8 %) had hyperthyroidism. There was a significantly increased frequency of associated neuropsychiatric manifestations (64.2 % vs 45.2 %;p = 0.012), and hematological involvement (32.1 % vs 14.3 %; p = 0.011) and less complement (C4) consumption (15.1 % vs 23.6 %; p = 0.048) in those with thyroid dysfunction compared to those without. The frequency of anti-double stranded deoxyribonucleic acid (anti-dsDNA) positivity tended to be higher (71.7 % vs 66.8 %) and its titre significantly lower (316.3 ± 319.8 IU/ml vs 461.3 ± 459.1; p = 0.013). The SLICC-DI and mortality rate were comparable between groups. Only the presence of proteinuria inversely significantly correlated with the level of T4 (r = -0.31, p = 0.007). ConclusionThyroid dysfunction forms a notable percentage of SLE patients with a prominent female gender propensity. The significant relation of thyroid disorders to neuropsychiatric manifestations, hematological involvement, C4 consumption and anti-dsDNA deserves recognition.

Towards a disease prediction system: BioBERT-based medical profile representation

&lt;p&gt;Healthcare professionals are increasingly interested in predicting diseases before they manifest, as this can prevent more serious health conditions and even save lives. Machine learning techniques are now playing an important role in healthcare, including in the early prediction of diseases based on prior medical knowledge. However, one of the biggest challenges is how to represent medical information in a way that can be processed by machine learning algorithms. Medical histories are often in a format that computers cannot read, so filtering and converting this information into numerical representations is a crucial step. This process has become easier with the advancement of natural language processing techniques. In this paper, we propose three representations of medical information, two of which are based on BioBERT, the latest text representation techniques for the biomedical sector. The efficiency of these representations is tested on the MIMIC-III database, which contains information on 46,520 patients. The focus of the study is on predicting Coronary Artery Disease, and the results demonstrate the effectiveness of the proposed approach. The study highlights the importance of medical history in disease prediction and demonstrates the potential of machine learning techniques to advance healthcare.&lt;/p&gt;

Inaptitude ophtalmologique dans les armées : étude épidémiologique multicentrique

This article aims to describe the causes of ophthalmological disqualification from the military services detected during specialist consultations conducted at Army Training Hospitals. This observational, cross-sectional, multicenter study retrospectively included individuals deemed as "unfit for military service" due to eye diseases identified during the specialist consultation conducted at 3 ATHs between January 2020 and December 2021. The data collected included age, medical and surgical history, reasons for ophthalmological disqualification, uncorrected distance visual acuity, best corrected distance visual acuity and cycloplegic refraction. Over this period, 133 subjects (98 men and 35 women) were included. Thirty-eight candidates (28.6%) were declared unfit due to a refractive error beyond the required limits, including 30 myopic subjects in excess of -10 diopters (D) and 8 hypermetropic subjects over +8 D. Twenty-five candidates (18.8%) were unfit under the age of 21 years due to corneal refractive surgery performed before the required age. Four subjects (3.0%) were unfit due to phakic intraocular lenses. Degenerative conditions were observed in 23 subjects (17.3%), including 21 patients with severe keratoconus. Other causes of incapacity were linked to oculo-orbital trauma in 11 subjects (8.3%), moderate or severe amblyopia in 7 patients (5.3%), congenital causes in 7 subjects (5.3%), inflammatory or infectious diseases in 7 candidates (5.3%), hereditary causes in 6 subjects (4.5%) and undetermined visual dysfunctions in 4 subjects (3.0%). The three main causes of ophthalmological disqualification were high ametropia, refractive surgery performed before the required age and keratoconus.

Nghiên cứu cộng đồng về một số yếu tố dịch tễ và hiểu biết của cha/mẹ khi sử dụng kháng sinh cho trẻ tại Thái Nguyên và Hà Giang năm 2020-2021

This study was conducted in 2020-2021 to investigate several epidemiological factors and parents' knowledge of antibiotic use in children in Thai Nguyen and Ha Giang. Methods: 459 eligible subjects were investigated for epidemiological information and knowledge of antibiotic use. 275 bacterial culture were successfully done from nasopharyl swabs of participants. Results: Some epidemiological factors such as parents' smoking, a high percentage of pipe tobacco smoking (21.6%), and up to 30.7% of relatives living in the same house (including Ha Giang at 31.4% and Thai Nguyen at 30.0%). In children, medical history of tonsillitis accounted for 37.3%, with otitis media accounting for 11.5%. The average influenza incidence was 1.7 times per year in Ha Giang and 1.9 times per year in Thai Nguyen. The proportion of children with antibiotic consumption over the past year was 47.3% in Thai Nguyen and 34.7% in Ha Giang. Commonly used antibiotics were amoxicillin and cephalexin, and most parents did not remember or knew the names of the antibiotics their children were taking. There were 42.0% of parents who knew about the use of antibiotics that the effects of antibiotics could treat bacteria. This rate was 46.9% in Ha Giang and 36.8% in Thai Nguyen, both statistical significance with p&lt; 0.05. Of those 275 bacterial culture from nasopharyl swab, 78 (28.4%) was detected S. pneumoniae, which concordance with previous literature review. Recommendations: Conduct communication and education programs to better understand the use of antibiotics and reduce risk factors for respiratory infections. Further investigation, intervention may needed to reduce risk in S. pneumoniae carriers.

Artificial neural networks analysis predicts long-term fistula function in hemodialysis patients following percutaneous transluminal angioplasty

Kidney failure is particularly common in the United States, where it affects over 700,000 individuals. It is typically treated through repeated sessions of hemodialysis to filter and clean the blood. Hemodialysis requires vascular access, in about 70% of cases through an arteriovenous fistula (AVF) surgically created by connecting an artery and vein. AVF take 6 weeks or more to mature. Mature fistulae often require intervention, most often percutaneous transluminal angioplasty (PTA), also known as fistulaplasty, to maintain the patency of the fistula. PTA is also the first-line intervention to restore blood flow and prolong the use of an AVF, and many patients undergo the procedure multiple times. Although PTA is important for AVF maturation and maintenance, research into predictive models of AVF function following PTA has been limited. Therefore, in this paper we hypothesize that based on patient-specific information collected during PTA, a predictive model can be created to help improve treatment planning. We test a set of rich, multimodal data from 28 patients that includes medical history, AVF blood flow, and interventional angiographic imaging (specifically excluding any post-PTA measurements) and build deep hybrid neural networks. A hybrid model combining a 3D convolutional neural network with a multi-layer perceptron to classify AVF was established. We found using this model that we were able to identify the association between different factors and evaluate whether the PTA procedure can maintain primary patency for more than 3 months. The testing accuracy achieved was 0.75 with a weighted F1-score of 0.75, and AUROC of 0.75. These results indicate that evaluating multimodal clinical data using artificial neural networks can predict the outcome of PTA. These initial findings suggest that the hybrid model combining clinical data, imaging and hemodynamic analysis can be useful to treatment planning for hemodialysis. Further study based on a large cohort is needed to refine the accuracy and model efficiency.

Supervised and unsupervised learning models for pharmaceutical drug rating and classification using consumer generated reviews

Optimization of medication therapy depends on maximizing benefits and minimizing side effects of medications. This research showed how a joint approach using text mining, natural language processing, and machine learning can provide information for personalized and optimized medication therapy. Reviews on the benefits and side effects of prescription and over-the-counter medications were used to determine how well an integrated supervised and unsupervised learning could learn medication satisfaction. Supervised learning with naïve-Bayes, non-linear support vector machine with radial basis function kernels, and random forests with CART decision trees was measured by a micro-aggregated Matthews correlation coefficient and a macro-averaged F1 measure. Random forests outperformed support vector machines by almost 250% and naive-Bayes by 600% on the two evaluation metrics. All models did better with three rating levels, instead of five. Topic modelling and stacked cluster analysis were coupled with parts-of-speech tagging and text mining operations to establish a robust data preprocessing procedure to eliminate noisy features from the data. Unsupervised topic modelling and clustering represented an exploratory validation of how easy supervised classification would be. Well-defined latent topics were discovered including topics on “sleep quality”, “the opportunity to get back to work”, and “weight gain”. Overlapping clusters revealed that incorporating more information on social, demographic, or medical history variables could improve classifier performance. This research provided evidence that medication satisfaction can be learned with carefully designed joint supervised, unsupervised, and natural language learning techniques.

Health Insurance Cost Prediction Using Machine Learning

Abstract: Healthcare expenditure is a critical concern worldwide, and accurate prediction of health insurance costs can aid in effective resource allocation and risk management. In this project, we employ machine learning (ML) techniques to develop a predictive model forestimating health insurance costs. The dataset used comprises various demographic, lifestyle, andmedical information of insured individuals,including lifetime, gender, QTI, smoking habits, area, and medical history.

Sepsis Prediction

Abstract: Sepsis manifests as a life-threatening condition wherein the body's response to infection triggers organ dysfunction. Typically, this immune response inadvertently damages the body's tissues while combating the infection. Within the overall range of sepsis, septic shock represents a critical subset characterized by notable circulatory, cellular, and metabolic abnormalities, resulting in a increased mortality rate than standard sepsis. Noteworthy is the consensus from various studies highlighting the crucial role of early intervention in enhancing the survival chances of sepsis patients. Given the urgency of timely recognition and treatment, there is a heightened focus on sepsis prediction. Traditional scoring systems such as APACHE, SAPS, and SOFA provide insights into disease severity and prognosis but lack the capacity for early sepsis detection. This study uses ML techniques and analyzes extensive patient data, which includes vital signs and medical history, to identify important indicators and prognostic patterns that find the occurrence of sepsis. By applying advanced algorithms like neural networks and ensemble methods, the model shows impressive accuracy in predicting the progression of sepsis. In addition, detailed analysis of trait significance helps to understand key prognostic markers, allowing clinicians to strategically prioritize interventions. Successful integration of this prognostic tool into the clinical setting offers a promising opportunity for preventive management of sepsis, which can reduce mortality and improve patient outcomes. We built a (LSTM) network to distinguish the internal association between different. indicators in clinical data. Analysis includes use of electronic health records, multicenter collaboration, and challenge development. Our goal is to support the research of accurate and timely sepsis predictors, ultimately improving patient outcomes.

Revolutionizing Disease Prediction with Deep Learning and Predictive Analysis

Abstract: The integration of deep learning and predictive analysis has emerged as a powerful tool in the healthcare improvement effort, revolutionizing the process of illness prediction. The purpose of this project is to develop a comprehensive framework that can precisely predict and identify a range of diseases at an early stage using these technologies. Deep learning algorithms are used to uncover complicated patterns and connections that may point to the onset of a disease by evaluating massive datasets that include patient demographics, medical history, genetic data, and environmental factors. The predictive capabilities of these algorithms enable healthcare practitioners to more precisely identify potential health issues, enabling preventive intervention and customized treatment regimens. Moreover, putting this idea into reality promises to improve patient outcomes, substantially reduce healthcare costs, and ultimately alter the landscape of preventative medicine.

Streamlining Dental Clinic Operations Introducing the Web Application for Efficient Dental Clinic Management

Abstract: The Dental Clinic Management System web application is a comprehensive software program designed to automate and optimize the various tasks and operations that occur in a dental clinic. Historically, dental clinics have kept their records manually and done their paperwork on paper, which has led to many errors, inefficiencies, and delays.To tackle these problems, the Web Application for Dental Clinic Management System provides dental professionals and personnel with an easy-to-use interface that makes daily operations more efficient. The DCMS web application assists patients in scheduling a dentist appointment at a dental clinic. The system simplifies the administration of patient data, encompassing personal data, medical history, and treatment documentation. Precise diagnosis and treatment planning are ensured by the easily accessible and updated information by authorized staff. Only the patient that have been registered in Dental Clinic Management System be able to use the system. It is run by highly qualified and experienced health professionals.This website covers Admin panel, Dentist panel and Patient panel. User can perform search by dentist specialty, gender, and dentist name. User can browse through dentist profile and view their specialty information. The register user can choose and book an appointment at the flexible time and date. Dentist can view the patient appointment and they can confirm or cancel their reservation. Admin can able control and maintain Dentist and patient information through a computerized process. And Admin can view and replay the unregistered users queries, also view the medical report of the patient.

Early Prediction of Liver Disease

Abstract: Chronic liver disease (CLD) poses a substantial global health challenge, leading to considerable morbidity and mortality. The timely identification of CLD is imperative to enhance patient outcomes and ensure effective disease management. This research introduces an innovative machine learning framework designed to predict liver disease at an early stage, utilizing a wide range of clinical data resources. By integrating demographic information, laboratory test results, imaging findings, and patient medical history, our model aims to accurately forecast the onset and progression of CLD. Advanced classification algorithms, including Random Forest and Gradient Boosting, are employed for feature selection and model development. Performance evaluation is conducted on a comprehensive dataset comprising longitudinal patient records. The results demonstrate promising accuracy, sensitivity, and specificity, highlighting the potential of machine learning in enhancing CLD risk assessment and enabling timely interventions.

Neuroanatomical alterations in brain disorder: A magnetic resonance imaging analysis

Background: Bipolar disorder is a serious mental disorder caused by strong mood fluctuations, affecting 2% of the world's population. People with bipolar disorder experience both manic and depressive episodes, which can lead to suicidal thoughts and changes in appetite, activity, and focus. There are different subtypes of bipolar disorder, with cyclothymic disorder being the milder of the two. Bipolar I is characterized by manic periods, while bipolar II is marked by hypomanic and significant depressive episodes. Bipolar disorder is a biological, genetic, and environmental condition that can affect anyone at any age. Methods: In this study, we enrolled a total of 49 bipolar disorder (BD) participants to 44 healthy control (HC) subjects. Bipolar disorder patients were enlisted with their consent using web portals, medical facilities, and local marketing. MRI contraindications, medical history, self-reported using a mood-altering medication, inadequate vision, and left-handedness were among the exclusion criteria. The various tissue structures which are: the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) were examined using voxel-based morphometry (VBM) to ascertain these changes. We employed statistical parametric mapping (SPM) for both image processing and statistical analysis. Results: Our findings demonstrate that individuals with bipolar disorder showed a substantial volume decrease in both gray matter (GM) and white matter (WM), with gray matter (GM) and white matter (WM) volume decreases being more noticeable in the frontal, temporal, parietal, and occipital lobes. Additionally, individuals with bipolar disorder showed no CSF volume in any of the brain regions. Conclusion: Our VBM research backs up earlier studies that link changes in brain structure, particularly gray matter and white matter, to bipolar disorder. These discoveries shed more information on the neurology of bipolar disorder and could guide the creation of more potent diagnostic and treatment modalities.

Indexalization of the Medius using the Chase technique in squamous cell carcinoma of the index finger: report of a case and review of the literature

Introduction: Squamous cell carcinoma is a rare and aggressive lesion. Bone localization is exceptional both in current practice and in the literature. The Chase technique, a surgical technique involving amputation of the index finger with indexing of the middle finger, is used both in oncological surgery and in traumatic surgery. Material and method: We report the observation of a sixty-year-old patient, with no medical or surgical history, who presented for consultation for swelling of the index finger that had been developing for almost four months associated with pain. Technique: The intervention performed was the Chase technique, (amputation of the 2nd ray) which took place successively in a skin phase, a tendon phase and a bone phase. Result: Early rehabilitation was required with good aesthetic and functional results for the hand. Conclusion: Indexing the middle finger has the advantage of ensuring maximum opening of the first corner, minimizing functional discomfort and maintaining good hand strength.

A review on Gymnosporia rothiana of its botanical, ecological, and medicinal significance

Gymnosporia rothiana, also known as "Konkan Katai" or "Roth's Spike Thorn," is a rare and endangered plant species in Maharashtra, India. Its botanical description includes thorny branches, elliptical leaves, and small white flowers. Ecologically, it supports habitat formation, erosion control, and biodiversity. Traditional healers use Gymnosporia rothiana for its anti-inflammatory, digestive, and febrifuge properties, highlighting its cultural significance in indigenous medicine. Gymnosporia rothiana, a valuable botanical resource with ecological, cultural, and medicinal importance, can be harnessed for human health and biodiversity conservation in Maharashtra, India, through research, conservation efforts, and advancements in phytochemical analysis, pharmacological studies, and clinical trials.In the rough and tumble of Maharashtra, India, Gymnosporia rothiana, also called "Konkan Katai" or "Roth's Spike Thorn," is a botanical gem. This essay provides a thorough analysis of Gymnosporia rothiana, including its ecological importance, traditional therapeutic use, intricate botanical details, and potential directions for further study and preservantion. The botanical description clarifies Gymnosporia rothiana's remarkable characteristics, which include its exquisite white blooms, elliptical leaves, and thorny branches. In terms of ecology, this plant is a cornerstone in its natural environments, helping to maintain soil stability, reduce erosion, and enhance biodiversity in the arid conditions of rocky plateaus and scrub woods. Gymnosporia rothiana is highly respected in traditional medicine, where it has been used for centuries by healers to treat a wide range of conditions. Gymnosporia rothiana represents a complex tapestry of medical history profoundly embedded in Maharashtra's cultural past, from its supposed anti-inflammatory and digestive capabilities to its function infever control and woundhealing. In the future, there may be more opportunities to explore the pharmacological potential and complex biochemistry of Gymnosporia rothiana. Through phytochemical analysis, pharmacological investigations, and clinical trials, scientists want to uncover the medicinal benefits concealed in this remarkable plant, therefore facilitating the development of evidence-based herbal remedies and environmentally sound conservation tactics.

Posterior reversible encephalopathy syndrome with functional and motor sequelae: A case report

Posterior Reversible Encephalopathy Syndrome (PRES) is a rare clinicoradiological neurological disorder, initially elucidated in 1996, Since this first description of PRES numerous case reports and case series, as well as retrospective observational studies describing the syndrome have been published. Importantly, no randomized controlled studies have been performed, We present the case of a young patient with no significant medical history who presented with neurological symptoms consistent with Posterior Reversible Encephalopathy Syndrome (PRES). Brain MRI confirmed the diagnosis of Posterior Reversible Encephalopathy Syndrome (PRES). Additionally, the etiological assessment yielded negative results. A notable aspect in our patient's case is the persistence of sequelae, characterized by dexterity and grip impairment on the right side, hindering various activities of daily living. The diagnosis of PRES is both radiological and clinical, with MRI playing a crucial role. Treatment is primarily symptomatic, sometimes requiring intensive care unit admission. Prognosis is favorable in the majority of cases, but severe complications and even death can occur. The reversibility of initial symptoms is well-documented in the literature. Long-term functional impairment outcomes following PRES have not been adequately characterized in the literature. Therefore, it is necessary to conduct follow-up screenings in more patients presenting both typical and atypical PRES syndromes. This will provide insight into whether the functional and motor sequelae of PRES are less reversible.

Unusual presentation of axillary abscess caused by Salmonella spp. in a middle-aged woman: A case report

We present a case of a 57-year-old woman with no significant medical history who presented with a painful axillary mass associated with fever. Initial treatment with antibiotics failed to yield improvement, leading to hospital admission. Subsequent investigation revealed an axillary abscess caused by Salmonella spp., a rare etiology for such presentation. The patient responded favorably to antibiotic therapy with ceftriaxone. This case underscores the importance of considering uncommon pathogens in the differential diagnosis of soft tissue abscesses, especially in cases refractory to standard treatment.

Predict the effect of the number of positive preoperative serum tumor markers on the surgical method and prognosis of intrahepatic cholangiocarcinoma patients based on mediation analysis

Objectives: To investigate the effect of the number of positive preoperative serological tumor markers on the surgical approach and prognosis of patients with intrahepatic cholangiocarcinoma. Methods: This is a retrospective case-series study. Data from 548 patients with intrahepatic cholangiocarcinoma after radical resection from October 2010 to April 2019 were retrospectively collected in 10 hospitals of China. There were 277 males and 271 females with an age of (57.8±10.2)years(range:23 to 84 years). Four hundred and twenty-six patients(77.7%) had at least one positive preoperative serum tumor marker. The data collection included the results of 4 preoperative serological tumor markers,other preoperative indicators(5 prodromal symptoms, 6 medical history,8 preoperative serological indicators,5 preoperative imaging indicators,and 14 preoperative pathological examination indicators),baseline data (gender and age),surgical methods,and prognostic follow-up data. Four preoperative results of serologic tumor marker and surgical procedure were converted into categorical variables. The number of positive preoperative serum tumor markers was used as the treatment variable,the surgical method was used as the mediating variable,and the survival time was used as the outcome variable. Univariate and multivariate analysis were used to screen for other preoperative indicators which were independent factors that influenced the surgical procedure and the prognosis of patients as covariates to analyze the mediating effect. Results: Of the 548 patients included in the study, 176 patients (32.1%) underwent partial hepatectomy,151 patients(27.5%) underwent hemihepatectomy, and 221 patients(40.3%) underwent partial hepatectomy or hemihepatectomy combined with other treatments. The results of the univariate and multivariate analysis showed that the number of positive serum tumor markers,intrahepatic bile duct dilatation,portal vein invasion,pathological differentiation,pathological type,vascular invasion,T stage,N stage and maximum tumor diameter were independent factors influencing the surgical procedure(all P<0.05). Intrahepatic bile duct dilatation,pathological differentiation and T stage were independent prognostic factors for patients with intrahepatic cholangiocarcinoma(all P<0.05). Intrahepatic bile duct dilatation,differentiation and T stage were included as covariates in the mediation effect model. The results showed that the number of positive serum tumor markers before surgery had a negative predictive effect on the survival time of patients with intrahepatic cholangiocarcinoma (β=-0.092, P=0.039),and had a positive predictive effect on the surgical method (β=0.244,P<0.01). The number of positive serum tumor markers had a negative predictive effect on the survival time of patients with intrahepatic cholangiocarcinoma (β=-0.151, P=0.002). Direct and indirect effects accounted for 71.3% and 28.7% of total effects,respectively. Conclusions: The higher the positive number of preoperative tumor markers,the worse the prognosis of patients with intrahepatic cholangiocarcinoma. The number of positive cells not only directly affects the prognosis of patients,but also indirectly affects the prognosis of patients by affecting the surgical method.

Cardiovascular and lifestyle risk factors of mild cognitive impairment in UK veterans and non-veterans.

The link between poor cardiovascular health (CVH), lifestyle and mild cognitive impairment (MCI) has been well established in the general population. However, there is limited research exploring these associations in ageing UK veterans. This study explored the risk of MCI and its association with nine CVH and lifestyle risk factors (including diabetes, heart disease, high cholesterol, high blood pressure, obesity, stroke, physical inactivity, the frequency of alcohol consumption and smoking) in UK veterans and non-veterans. This prospective cohort study comprised data from the PROTECT study between 2014 and 2022. Participants comprised of UK military veterans and non-veterans aged ≥50 years at baseline. Veteran status was defined using the Military Service History Questionnaire. CVH and lifestyle risk factors were defined using a combination of self-report measures, medication history or physical measurements. MCI was defined as the presence of subjective and objective cognitive impairment. Based on a sample of 9378 veterans (n = 488) and non-veterans (n = 8890), the findings showed the risk of MCI significantly reduced in veterans with obesity, those who frequently consumed alcohol and were physically inactive compared to non-veterans. The risk of MCI significantly increased in veterans with diabetes (hazards ratio [HR] = 2.22, 95% confidence interval [CI] 1.04-4.75, P ≤ 0.05) or high cholesterol (HR = 3.11, 95% CI 1.64-5.87, P ≤ 0.05) compared to veterans without. This study identified CVH and lifestyle factors of MCI in UK veterans and non-veterans. Further work is needed to understand these associations and the underpinning mechanisms which could determine intervention strategies to reduce the risk of MCI.

Seroprevalence of Anti-SARS-CoV-2 Antibodies Among Asymptomatic Healthcare Workers of Dezful Ganjavian Hospital, Southwest Iran

Background: The 21st century has seen the emergence of the COVID-19 pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Objectives: Given the elevated risk of COVID-19 transmission among healthcare workers, our objective was to assess the seroprevalence of anti-SARS-CoV-2 antibodies in asymptomatic healthcare workers at Dezful Ganjavian Hospital in 2021. Methods: A total of 276 participants were included in this descriptive cross-sectional study. The study assessed the participants' medical history, including autoimmune diseases, corticosteroid administration, and underlying conditions. Additionally, the history of previous SARS-CoV-2 infection and exposure to COVID-19 patients was recorded. Results: The results showed that 32.6% of the participants had a history of previous SARS-CoV-2 infection. No significant associations were found between the previous infection history and variables such as sex, age, corticosteroid administration, autoimmune disease, or exposure to COVID-19 patients. However, a significant association was observed between the participants' history of COVID-19 and underlying conditions. Serum analysis revealed that 10.1% of the participants tested positive for IgG anti-SARS-CoV-2 antibodies, while 1.8% tested positive for IgM antibodies. A significant relationship was found between the presence of IgG antibodies and a history of previous SARS-CoV-2 infection. Similarly, all participants with IgM antibodies had a history of previous infection. Conclusions: The study findings indicate that a significant proportion of asymptomatic healthcare workers at Dezful Ganjavian Hospital have been previously exposed to SARS-CoV-2. These results can inform infection control measures and help protect both healthcare workers and patients in the hospital setting.

Practical surgical technique using the SMISS approach for lip reduction in involuted lip infantile hemangiomas

BackgroundLip infantile hemangiomas tend to show less volumetric regression and are more susceptible to visible sequelae in the involuted stage. Some of them still require surgical management after propranolol therapy. This study aimed to evaluate the efficacy and safety of the Stepwise, Multi-Incisional, and Single-Stage (SMISS) approach applied to lip reduction for those with involuted lip hemangiomas.MethodsA retrospective review was performed to evaluate patients with lip hemangioma who received previous propranolol treatment and underwent the aforementioned procedure. Demographic characteristics, lesion morphology, and medical history were reviewed. The Visual Analog Scale was applied to assess the postoperative appearance. Complications within 12 months postoperatively were recorded.ResultsA total of 18 patients with lip hemangioma were eligible. All patients received oral propranolol therapy before surgery, with treatment duration ranging from 6.0 to 23.0 months. Their age at surgery ranged from 2.5 to 9.0 years. The median Visual Analog Scale scores were 8.0, ranging from 4.0 to 10.0. No severe complications were reported.ConclusionsThis modified technique based on the SMISS approach has proven reliable and effective in improving the aesthetic outcome for involuted lip infantile hemangiomas. Practical surgical techniques still play an important part in the propranolol era.