UNASSIGNED: Metabolic syndrome is a global health concern. It is a condition that includes a cluster of various risk factors for type 2 diabetes and cardiovascular disease. This quasi-experimental study investigates the effect of a nurse-led low-carbohydrate regimen on anthropometric and laboratory parameters in metabolic syndrome patients.
UNASSIGNED: The study used a quasi-experimental design conducted at the University of Mosul; 128 participants meeting the metabolic syndrome criteria were recruited and divided into the intervention and control groups. The intervention group received personalized counseling and support in implementing a low-carb regime, while the control group received standard advice. The study participants were assessed by anthropometry, and laboratory parameters were evaluated pre- and post-intervention. Statistical data analysis was conducted using IBM-SPSS 27, including chi-square, Fisher\'s exact test, t-tests, and the Mcnemar test, which were performed to compare the changes within and between groups.
UNASSIGNED: The mean age of the participants in the intervention and control groups was 50.72 ± 6.43 years and 49.14 ± 6.89 years, respectively. Compared to the control group, the intervention group experienced a significant positive reduction in anthropometric measures and laboratory parameters, including weight, body mass index (BMI), waist circumference, lipid profiles, and HbA1c.
UNASSIGNED: A tangible effect of nurse-led interventions based on low-carbohydrate regimens in managing metabolic syndrome was empirically authenticated. Positive changes were observed in the intervention group regarding anthropometric measures and laboratory parameters. However, future research may require a larger sample size and a longer follow-up to confirm these effects and evaluate long-term metabolic impacts.

Diagnosis of relevant organ injury after blunt abdominal injury (AI) in multiple-injury/polytraumatised patients is challenging. AI can be distinguished between injuries of parenchymatous organs (POI) of the upper abdomen (liver, spleen) and bowel and mesenteric injuries (BMI). Still, such injuries may be associated with delays in diagnosis and treatment. The present study aimed to verify laboratory parameters, imaging diagnostics, physical examination and related injuries to predict intraabdominal injuries. This retrospective, single-centre study includes data from multiple-injury/polytraumatised patients between 2005 and 2017. Two main groups were defined with relevant abdominal injury (AI+) and without abdominal injury (AI-). The AI+ group was divided into three subgroups: BMI+, BMI+/POI+, and POI+. Groups were compared in a univariate analysis for significant differences. Logistic regression analysis was used to determine predictors for AI+, BMI+ and POI+. 26.3% (271 of 1032) of the included patients had an abdominal injury. Subgroups were composed of 4.7% (49 of 1032) BMI+, 4.7% (48 of 1032) BMI+/POI+ and 16.8% (174 of 1032) POI+. Pathological abdominal signs had a sensitivity of 48.7% and a specificity of 92.4% for AI+. Transaminases were significantly higher in cases of AI+. Pathological computed tomography (CT) (free fluid, parenchymal damage, Bowel Injury Prediction Score (BIPS), CT Grade > 4) was summarised and had a sensitivity of 94.8%, a specificity of 98%, positive predictive value (PPV) of 94.5% and, negative predictive value (NPV) of 98.2% for AI+. The detected predictors for AI+ were pathological abdominal findings (odds ratio (OR) 3.93), pathological multi-slice computed tomography (MSCT) (OR 668.9), alanine (ALAT) ≥ 1.23 µmol/ls (OR 2.35) and associated long bone fractures (OR 3.82). Pathological abdominal signs, pathological MSCT and lactate (LAC) levels ≥ 1.94 mmol/l could be calculated as significant risk factors for BMI+. For POI+ pathological abdominal MSCT, ASAT ≥ 1.73 µmol/ls and concomitant thoracic injuries had significant relevance. The study presents reliable risk factors for abdominal injury and its sub-entities. The predictors can be explained by the anatomy of the trunk and existing studies. Elevated transaminases predicted abdominal injury (AI+) and, specifically, the POI+. The pathological MSCT was the most reliable predictive parameter. However, it was essential to include further relevant parameters.

UNASSIGNED: Coronavirus disease 2019 (COVID-19) emerged as a global pandemic during 2019 to 2022. The gold standard method of detecting this disease is reverse transcription-polymerase chain reaction (RT-PCR). However, RT-PCR has a number of shortcomings. Hence, the objective is to propose a cheap and effective method of detecting COVID-19 infection by using machine learning (ML) techniques, which encompasses five basic parameters as an alternative to the costly RT-PCR.
UNASSIGNED: Two machine learning-based predictive models, namely, Artificial Neural Network (ANN) and Multivariate Adaptive Regression Splines (MARS), are designed for predicting COVID-19 infection as a cheaper and simpler alternative to RT-PCR utilizing five basic parameters [i.e., age, total leucocyte count, red blood cell count, platelet count, C-reactive protein (CRP)]. Each of these parameters was studied, and correlation is drawn with COVID-19 diagnosis and progression. These laboratory parameters were evaluated in 171 patients who presented with symptoms suspicious of COVID-19 in a hospital at Kharagpur, India, from April to August 2022. Out of a total of 171 patients, 88 and 83 were found to be COVID-19-negative and COVID-19-positive, respectively.
UNASSIGNED: The accuracies of the predicted class are found to be 97.06% and 91.18% for ANN and MARS, respectively. CRP is found to be the most significant input parameter. Finally, two predictive mathematical equations for each ML model are provided, which can be quite useful to detect the COVID-19 infection easily.
UNASSIGNED: It is expected that the present study will be useful to the medical practitioners for predicting the COVID-19 infection in patients based on only five very basic parameters.

UNASSIGNED: It is not always possible to differentiate between influenza and COVID-19 based on symptoms alone. This is a topic of significant importance as it aims to determine whether there are specific hematological parameters that can be used to distinguish between influenza and COVID-19 in children.
UNASSIGNED: Two hundred thirty-one children between the ages of 1 month and 18 years who presented to the children\'s outpatient clinic between June 2021 and June 2022 with similar symptoms and were tested with an influenza test and a COVID-19 PCR test were included in the study. Of the patients included in the study, 130 tested positive for COVID-19 and 101 positive for influenza. The patients were evaluated for hematological parameters.
UNASSIGNED: Age, eosinophils and monocyte factors were shown to be statistically significantly effective in COVID-19. The risk of COVID-19 increased 1,484-fold with age, 10,708-fold with increasing eosinophil count, and 1,591-fold with increasing monocyte count. The performance of the monocyte count and eosinophil count was assessed by receiver operating characteristic curve (ROC) analysis. According to the performed ROC analysis, the area under the curve (AUC) value was observed to be 0.990 for monocytes. According to the cutoff point >1.50, the sensitivity value was determined as 98.4% and the specificity value as 97.0%. AUC significance for eosinophils was found to be 0.989. According to the cutoff point >0.02, the sensitivity value was determined as 99.2% and the specificity value as 93.1%.
UNASSIGNED: In the diagnosis of COVID-19, the eosinophil count and monocyte count are easily accessible, inexpensive, and important parameters in terms of differential diagnosis and can help in the differentiation of COVID-19 from influenza during seasonal outbreaks of the latter. Developing parameters for clinicians to use in diagnosing COVID-19 and influenza can facilitate their work in practice.

BACKGROUND: Spinal muscular atrophy (SMA) is a progressive neurodegenerative disorder that can be treated with intrathecal nusinersen, an antisense oligonucleotide. In addition to efficacy, safety is a determining factor in the success of any therapy. Here, we aim to assess the safety of nusinersen therapy in paediatric patients with SMA.
METHODS: Laboratory data of paediatric patients with SMA who received nusinersen between October 2019 and May 2022 were retrospectively analysed.
RESULTS: During the observation period, 46 infants and children aged 2.9 months to 13.6 years received a total of 213 nusinersen doses without safety concerns. Inflammatory markers were stable throughout the study. International normalized ratio was increased by 0.09 per injection. Urea levels were increased by 0.108 mmol/L, and cystatin C decreased by 0.029 mg/L per injection. There were no significant changes in platelet count, activated partial thrombin time, creatinine levels or liver enzyme levels during treatment. The cerebrospinal fluid (CSF) leukocyte count remained stable, and total protein increased by 24.038 mg/L per injection.
CONCLUSIONS: Our data showed that nusinersen therapy is generally safe in children with SMA. Laboratory monitoring did not identify any persistent or significantly abnormal findings. CSF protein should be monitored to gain more insights.

BACKGROUND: Severe multisystem inflammatory syndrome in children (MIS-C) and severe dengue are challenging to identify during the COVID-19 pandemic in dengue-endemic areas. Fever, multiorgan involvement, and shock characterize both severe MIS-C and severe dengue. Distinguishing between the two diseases is beneficial in initiating proper management.
METHODS: Medical records of children < 18 years old who were hospitalized at Hasan Sadikin General Hospital\'s PICU between December 2020 and July 2022 with severe MIS-C or severe dengue were recorded. Differences were assessed using comparative and descriptive analyses.
RESULTS: Seventeen severe dengue patients and 4 severe MIS-C were included. The average age of severe MIS-C was 11.5 years (SD ± 2.9, 95% CI), and that of severe dengue patients was 6.2 years (SD ± 4.4, 95% CI) (p value = 0.034, 95%). Fever and abdominal pain were the most common symptoms in both groups (p = 0.471, 95% CI). Rash (p = 0.049) and nonpurulent conjunctivitis (p = 0.035) were two symptoms with significant differences. The highest platelet count (p-value = 0.006, 95% CI), AST (p-value = 0.026, 95% CI), and D-dimer level (p-value = 0.025, 95% CI) were significantly different between the two cohorts. Cardiac abnormalities were found in all (100%) severe MIS-C patients, but only one (5.9%) in severe dengue patients.
CONCLUSIONS: Age, rash, nonpurulent conjunctivitis, platelet count, AST and D-dimer level may distinguish severe MIS-C from severe dengue fever.

OBJECTIVE: The ongoing outbreak of the respiratory disease coronavirus disease 2019 (COVID-19) is currently presenting a major global health threat. This pandemic is unprecedented in recent human history. The objective of this study was to examine the relationship between cycle quantitation (Cq) and laboratory parameters in COVID-19 patients, aiming to determine if Cq levels can provide valuable insights into the COVID-19 disease.
METHODS: This study involved 234 participants who were divided into case and control groups. Real-time PCR tests were used to diagnose COVID-19 cases in the study participants. Blood tests, including complete blood count, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), lactate dehydrogenase (LDH), D-dimer, IgG, and IgM, were also conducted. Statistical analysis was performed using SPSS 22 software.
RESULTS: The findings showed that COVID-19-positive cases had significantly higher levels of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), D-dimer, ESR, CRP, and LDH compared to normal cases. Additionally, the case group had significantly lower lymphocyte and platelet counts. There was a statistically significant positive correlation between Cq levels and lymphocyte count (r = .124, p = .014). Conversely, there was a statistically significant inverse correlation between Cq levels and NLR (r = -.208, p = .017). Furthermore, the evaluation of hematological, inflammatory, and biochemical indexes in COVID-19 patients using the receiver-operating characteristics curve demonstrated statistically appropriate sensitivity and specificity.
CONCLUSIONS: Our outcomes indicated a significant association between Cq levels and PLR, NLR, D-dimer, CRP, and ESR in COVID-19 patients. Consequently, including the report of laboratory parameters alongside Cq values offers a promising prognosis.

OBJECTIVE: Lung cancer, the most common cause of cancer-related death, is diagnosed mostly in advanced stages, and 5-year survival is approximately 5.8%. It is critical to identify reliable prognostic factors to optimize treatment responses, guide therapeutic strategies and pave the way to new research. In this study, we aimed to investigate the strongest prognostic factors for advanced non-small cell lung cancer (NSCLC).
METHODS: We retrospectively analyzed 278 patients with NSCLC. We evaluated the association between potential prognostic factors and overall survival (OS) times using Kaplan-Meier analysis and Cox regression analysis.
RESULTS: The median OS in all patients was 15.3 months. In univariate analysis, gender, histologic type, performance status, immunotherapy, radiotherapy, hemoglobin level, serum albumin, sodium-globulin ratio (SGR), neutrophil-lymphocyte ratio (NLR), systemic immune inflammation index (SII), hemoglobin-albumin-lymphocyte-platelet score (HALP), and advanced lung cancer index (ALI) were associated with survival. Models were established for multivariate analyses. In the models, NLR, SGR, HALP, immunotherapy, radiotherapy, and Eastern Cooperative Oncology Group (ECOG) performance status showed independent prognostic features (p < 0.001, p = 0.003, p = 0.002, p < 0.001, p = 0.010, and p = 0.025, respectively). In addition, in the subgroup analysis, prognostic indexes (NLR, SGR, and HALP) were found to have a prognostic effect on survival in multiple subgroups.
CONCLUSIONS: Pretreatment NLR, SGR, HALP, immunotherapy, radiotherapy, and ECOG performance status are independent prognostic factors for advanced NSCLC patients. These prognostic factors can be used in clinical practice as easily accessible, simple, and useful tools for clinicians.

This study aims to investigate the clinical course and factors associated with hospital admission and mortality among SARS-CoV-2 patients within the Nairobi Metropolitan Area. The study utilizes a multicenter retrospective cohort design, collecting clinical characteristics and laboratory parameters of hospitalized patients from March 2020 to May 2022. Data analysis includes percentages, frequencies, chi-square tests, Kaplan-Meier analysis, pairwise comparisons, and multivariate regression models. Ethical considerations are observed throughout the research process. The study findings highlight significant associations between comorbidities, such as hypertension, and increased mortality risk due to COVID-19. Symptoms including fever, cough, dyspnea, chest pain, sore throat, and loss of smell/taste are also identified as predictors of mortality. Abnormal laboratory parameters, such as oxygen saturation, procalcitonin, glucose levels, serum creatinine, and gamma-glutamyl transpeptidase, are associated with mortality. However, demographic factors and certain vital signs do not exhibit significant associations. Recommendations based on this study suggest increased monitoring and management of comorbidities, early identification and management of symptoms, regular monitoring of laboratory parameters, continued research and collaboration, and implementation of preventive measures. Overall, a multidisciplinary approach involving healthcare professionals, researchers, policymakers, and the public is crucial to improve COVID-19 outcomes and reduce mortality rates. Adaptation of strategies based on emerging evidence and resource allocation is essential for effective management of the pandemic.

Understanding the factors driving SARS-CoV-2 infection progression and severity is complex due to the dynamic nature of human physiology. Therefore, we aimed to explore the severity risk indicators of SARS-CoV-2 through demographic data, clinical manifestations, and the profile of laboratory parameters. The study included 175 patients either hospitalized at King Abdulaziz Medical City-Riyadh or placed in quarantine at designated hotels in Riyadh, Saudi Arabia, from June 2020 to April 2021. Hospitalized patients were followed up through the first week of admission. Demographic data, clinical presentations, and laboratory results were retrieved from electronic patient records. Our results revealed that older age (OR: 1.1, CI: [1.1-1.12]; p < 0.0001), male gender (OR: 2.26, CI: [1.0-5.1]; p = 0.047), and blood urea nitrogen level (OR: 2.56, CI: [1.07-6.12]; p = 0.034) were potential predictors of severity level. In conclusion, the study showed that apart from laboratory parameters, age and gender could potentially predict the severity of SARS-CoV-2 infection in the early stages. To our knowledge, this study is the first in Saudi Arabia to explore the longitudinal profile of laboratory parameters among risk factors, shedding light on SARS-CoV-2 infection progression parameters.