The increase in handball category enhanced the majority of the physical capacities evaluated; however, these differences were reduced between the U16 vs. U18 categories. The indirect mediation effect suggests that handball category mediated the relationship between lean mass and upper and lower strength, velocity, agility, and cardiorespiratory fitness. In contrast, an indirect effect suggests that category mediated the relationship between fat mass only in agility and cardiorespiratory fitness. We conclude that U18s showed better body composition parameters, as well as better physical performance scores. Handball category clearly mediated the relationship between body composition through lean mass and fat mass and the physiological profile in handball youth, but lean mass proved to be more relevant when mediating physical performance.The aims of the present study were (1) to determine the differences in body composition and the physical and physiological profile of handball youths across age categories, and (2) to analyze the mediation effect of different categories on the relationship between lean mass or fat mass and specific physical handball capacities. Fifty-four young handball players aged 13 to 18 were assigned to U14 (13- and 14-year-olds), U16 (15- and 16-year-olds), and U18 (17- and 18-year-olds). Body composition was measured using multi-frequency bioelectrical impedance . Handball physical fitness was assessed using handgrip force, jumping tests , a 30-m sprint, a change-of-direction test , and a Yo-Yo intermittent recovery test level 1. Simple mediation analysis was performed to analyze whether the category mediated the relationship between lean mass or fat mass and physical capacities. No significant differences were observed according to category for the majority of the measured parameters, except for height, lean body mass, and arm span. Lean body mass increased significantly as player category increased ( Researchers in sport performance and talent identification are becoming increasingly focused on the study of sport-specific characteristics and requirements . HandbalTo date, different studies have evaluated different qualities considered to be decisive in handball youths, such as strength, power, speed, and agility, as well as high-intensity intermittent aerobic training ,7,8,9,10As mentioned above, anthropometric characteristics have been shown to be decisive in youth and adult team handball ,12,14. TThe present investigation aimed (1) to determine the differences in body composition (lean body mass or fat mass) and the physical and physiological profile of handball youths across age categories, and (2) to analyze the mediation effect of different categories on the relationship between lean mass or fat mass and specific physical handball capacities. Our initial hypotheses were that body composition and physical performance would develop over the age categories, and that the age category would mediate differently the relationship between lean mass and fat mass and the performance-determining physical capacities in young handball players.The present study enrolled a total of 54 male handball players aged 13–18 from Pedro Alonso Niño Handball Club located in the Huelva region. The 54 players were divided into three different categories by birth year: the youngest group was born in 2005–2006 and played in the 13–14-year-old category (U14), the second group was born in 2003–2004 and played in the 15–16-year-old category (U16), and the oldest group was born in 2001–2002 and played in the 17–18-year-old category (U18). Handball players were matched and competed by chronological age according to the Handball Federation rules. All players were in good health and passed a medical examination before starting their handball season. Measurements were taken during the competitive phase of the handball season. During this period, the training volume for the players consisted of 6.2 ± 2.1 h/week, which included integrated conditioning exercises in addition to weekend matches. All players and coaches from the different categories were previously informed, and informed parental consent and player assent were obtained. The procedures followed the rules established by the Declaration of Helsinki. The present research was approved by the Research Ethics Committee of the University of Huelva.A multidimensional test battery was used, including general as well as handball-specific tests. The tests consisted of body composition and anthropometrical determination, and physical fitness tests were included as part of their respective training periodization. All measurements were taken in the Sports Science Lab located in the Department of Sport Sciences at the University of Huelva. Physical assessment consisted of a lower and upper limp strength test, speed-agility tests, and a cardiorespiratory endurance test for each participant. All players were scheduled over three consecutive training days to take the different measurements without negative inferences between the performance tests. All of the subjects from the same handball team were called to perform the tests at the same time and on the same day. The measurement protocol schedule was: Monday, body composition and anthropometric determination and 30-m shuttle run test; Wednesday, dynamometry tests and Bosco test; and Thursday, agility T-test and Yo-Yo intermittent recovery test level 1.Height was determined with a stadiometer . While this was done, players remained static in an anatomical position with their heels together. The highest point of their head was taken as a reference, with their hair remaining compressed. Body composition measurements were taken by multi-frequency bioelectrical impedance . Subjects were placed barefoot in an anatomical position with their hands and feet on the electrodes, wearing light clothes and without metallic accessories . This position was maintained until the evaluator finished the test. The analyzer complied with the applicable European standards  for use in the medical industry. The impedance between the body segments was measured while an alternating current  passed through the lower and upper body. Participants were informed in advance of the conditions that had to be sustained prior to measurement: no alcohol for at least 24 h before the measurement, no vigorous exercise for at least 12 h prior, no food or drink for at least 3 h prior, and urination immediately before the measurement. All measurements took place at the same time. The following measurements were taken: weight, body mass index, and lean mass and fat mass expressed as a percentage of total fat in the body. Arm span (0.1 cm) was assessed using a measuring tape . The same researcher took all anthropometric measurements during the anthropometrical determination.The physical fitness measurements consisted of specific tests assessing lower and upper limb strength, speed and agility, and cardiorespiratory fitness ,13,16,17Strength measurements. To assess upper limb strength, a handgrip strength test was performed bilaterally using a TKK5101 Grip D-class III dynamometer , accurate to 0.1 kg and complying with standardization standards . Each paTo assess the strength of the lower limbs, the Bosco test was performed, evaluating three different jumps: squat jump (SJ), countermovement jump (CMJ), and Abalakov jump (ABK)  . Prior tSpeed and coordination characteristics were evaluated by a 30-m shuttle run and agility T-test, respectively . For the test, three photoelectric cells were located at 0, 5, and 30 m that recorded the player’s path at these determined distances. Acceleration speed and displacement speeds were measured. Acceleration was determined by the average speed between the starting cell and the first cell at 5 m. Displacement speed was determined by the average speed between starting point cell and the 30 m cell. Each player started from a standing position, with the front foot 0.2 m from the first photocell beam. Two separate attempts were performed with a recovery time of 120 s between them. The best attempt was used for the analysis.Handball agility/coordination was tested with a previously validated handball-specific modified version of the T-test . The modTo assess intermittent endurance performance, the players completed the Yo-Yo intermittent recovery test level 1 (Yo-Yo IR1) . The Yo-2 max consisted of the results obtained in the Yo-Yo IR1. The standardized (β) and unstandardized (B) regression coefficients are presented for four equations: (a) the equation that regressed the mediator (age category) on the independent variable (lean mass or fat mass), (b) the equation that regressed the dependent variable  and the mediator, (c) the equation that regressed the independent variable and the dependent variable, and finally (d) the equation that regressed the IE of the mediator (age category) on the relationship between the independent variable (lean mass or fat mass) and the dependent variable .Data were analyzed using SPSS 25.0 for Windows  and are expressed as mean values and standard deviation for the descriptive analysis. The standardizing of the variables was carried out using the Shapiro–Wilk test with Lilliefors correction. Homoscedasticity was determined with the Levene test. A one-way ANOVA test was performed to compare the differences across the three handball age categories. The effect sizes between categories were calculated using Cohen’s d, and the effect sizes were interpreted as small (d = 0.2 and 0.5), moderate (d = 0.5 and 0.8), and large (d > 0.8), with effect sizes inferior to 0.2 considered trivial . The levp < 0.05; ∆ = 4.66–9.38; effect size (ES) = 0.96–1.92).p < 0.05) between the three categories in both the right and left handgrip, as well as the SJ, CMJ, and ABK. For handgrip strength, the U18 category showed the highest values . In both the SJ and CMJ, significant differences were observed among all three categories in relation to jump height and power, with significant differences being found between U18 vs. U14 and U18 vs. U16. For the ABK, significant differences in jump height were found between the U18 vs. U14 and U18 vs. U16 categories. In contrast, significant differences were only found between U18 vs. U14 with respect to jumping power.p < 0.05). However, no significant differences were found between the U18 vs. U16 categories. More specifically, in the T-test, significant differences were obtained between the U18 vs. U14 and U18 vs. U16 categories for both the completion time  and the speed  reached by the subjects. Similarly, the results regarding the 30-m speed test showed that, as age increased, the speed reached by the players was greater (p < 0.05) for both the global test and for the test over 5-m and 25-m sections.The speed and coordination characteristics of the handball players were assessed using the 30-m shuttle run and agility T-tests. In both speed and agility tests, players’ coordination and velocity improved as the category increased . However, weight, BMI, and fat percentage were negatively associated with jump height  . For the speed and agility/coordination tests, weight, BMI, and fat percentage were negatively associated with speed  and positively associated with time. Likewise, weight, BMI, and fat percentage were negatively associated with the number of laps and VO2 max . Regarding U16, height, lean mass, and arm span were positively correlated with handgrip in both hands . Similarly, body mass, BMI, and lean mass were positively correlated with jump height (SJ and CMJ) . For the speed test, fat percentage was negatively associated with speed and positively associated with time . Finally, in the U18 category, a positive correlation was found between right handgrip and weight, BMI, and lean mass . Moreover, CMJ power was positively correlated with BMI  and there was a negative correlation between fat mass and 30-m speed . The correlation analyses in all samples indicated that age was positively correlated with better scores for all physical–physiological parameters. Additionally, greater lean mass was positively correlated with better scores for all physical condition tests . However, a higher fat percentage negatively influenced lower limb strength, speed, agility/coordination, and cardiorespiratory tests .p < 0.05). Moreover, the second equation (b) for category and the different physical–physiological dimensions showed a direct correlation with upper and lower limb strength  and cardiorespiratory fitness , and was negatively associated with time in the speed and agility tests . Additionally, the bootstrapped CIs for the indirect effect suggest that handball category mediated the relationship between lean mass and all physical capacities (p < 0.05). The second equation (b) for handball category and physical capacities showed a direct correlation with upper and lower limb strength , as well as cardiorespiratory fitness , and was negatively related to time in the speed and agility tests . Finally, the bootstrapped CIs for the indirect effect suggest that handball category mediated the relationship between fat mass in both agility  and cardiorespiratory capacity   B, and 28In recent years, studies have noted the importance of assessing the physical performance of young handball players from different countries and the influence of age, maturity status, and anthropometric characteristics upon their performance ,10,13,24The importance in determining body composition is a key factor in the characterization of a specific anthropometrical profile in both young and adult handball athletes. Several studies have paid special attention to determining their relationship based on age or biological maturity ,8,9, spe2 max comparison, especially in our U18 category, although the last study mentioned used the 20-m shuttle run test without considering the intermittent activity that handball presents, so its values may have been overestimated. Finally, the results obtained in the T-test revealed a better score compared to lower categories; however, no significant differences were observed between the two upper categories. This confirms that young players do not necessarily increase differences with regard to their physical performance as their specialization stage progresses, so that the highest profits would be at the beginning of the specialization stage, and as specialization progresses, these differences would be reduced between categories. In this line, it has been described that the morphological characteristics also tend to stabilize at the age of 16–17 years, at least in European children [Handball players should be able to perform repeated high-intensity actions; therefore, a well-developed physical–physiological profile is considered a determinant for handball players. We observed that our players exhibited slightly lower values for all tests performed, except the 30-m sprint test, where our results slightly exceeded those obtained by other studies in similar groups ,13,16,17children .In handball, young athletes perform better according to their anthropometrics and physiological characteristics ,26. AlthFor future research, it would be interesting to explore the evolution of key body compartments such as lean body mass in conjunction with age or degree of maturity, category, and specific position in the athletes’ performance. In this respect, recent studies  have indTo conclude, the results of the present study showed a progressive increase in all body composition parameters, mainly influenced by age, except for lean mass, as well as an improvement in strength, speed, agility, and cardiorespiratory capacity as the category increased. Handball age category presented an indirect mediating effect between lean mass and physical performance measured through upper and lower body strength, speed, agility, and cardio-respiratory fitness. Therefore, these results demonstrate a greater usefulness of the analysis of lean mass over even fat mass, given the greater association with physical performance.


The mortality rate from acute respiratory distress syndrome (ARDS) is high among hospitalized patients with coronavirus disease 2019 (COVID-19). Hence, risk evaluation tools are required to immediately identify high-risk patients upon admission for early intervention.A cohort of 220 consecutive patients with COVID-19 were included in this study. To analyze the risk factors of ARDS, data obtained from approximately 70% of the participants were randomly selected and used as training dataset to establish a logistic regression model. Meanwhile, data obtained from the remaining 30% of the participants were used as test dataset to validate the effect of the model.P Value = 0.437). The model had excellent discrimination and calibration during internal validation.Lactate dehydrogenase, blood urea nitrogen, D-dimer, procalcitonin, and ferritin levels were included in the risk score system and were assigned a score of 25, 15, 34, 20, and 24, respectively. The cutoff value for the total score was > 35, with a sensitivity of 100.00% and specificity of 81.20%. The area under the receiver operating characteristic curve and the Hosmer–Lemeshow test were 0.967  and 0.437(The novel risk score may be a valuable risk evaluation tool for screening patients with COVID-19 who are at high risk of ARDS.The online version contains supplementary material available at 10.1186/s12879-020-05561-y. In late December 2019, an outbreak of pneumonia of unknown etiology first occurred inWuhan, China, and rapidly spread worldwide , 4. ThenThis was a retrospective, observational cohort study performed at the isolation ward of the Department of Respiratory and Critical Care Medicine, Wuhan Union Hospital . We included all adult patients who were diagnosed with COVID-19, according to the WHO interim guidance, and who were discharged or died between January 10, 2020, and March 5, 2020. Since Wuhan Union Hospital has been a designated hospital for treating patients with COVID-19 since January 10, 2020, the population constituted a representative sample of all patients with COVID-19 seeking treatment. This study was approved by the research ethics commission of Wuhan Union Hospital (KY-2020-0040), and the need for informed consent was waived. Demographic, clinical, laboratory, and imaging data were extracted from the electronic medical record system of Wuhan Union Hospital through a standardized data collection form. All data were checked by two physicians (YM and JT), and a third researcher (MH) reviewed and made corrections to any differences in data.COVID-19 was confirmed based on the examination of respiratory specimens using real-time reverse transcription polymerase chain reaction (RT-PCR). The examination was performed at the clinical laboratory of Wuhan Union Hospital, which is a qualified institution for nucleic acid testing. Patients who were discharged met all of the following criteria: 1) absence of fever for at least 3 days, 2) notable improvement of findings on chest computed tomography (CT) scan, 3) remission of respiratory symptoms, and 4) two continuous negative results of SARS-CoV-2 RNA using throat swab samples collected at least 24 h apart.Fever was defined as an axillary temperature of at least 37.3 °C. ARDS was diagnosed according to the Berlin Definition . In brieRoutine blood examinations, including complete blood count, liver function, blood lipids, fasting blood glucose, kidney function, uric acid, lactate dehydrogenase, creatine kinase, and assessment of myocardial enzymes, coagulation profile, serum C-reactive protein (CRP) level, erythrocyte sedimentation rate (ESR), serum procalcitonin (PCT) level, and ferritin level, were performed upon patient admission, and re-examination was conducted at least once every 3 days during hospitalization. All patients who were admitted underwent chest CT scans.Patients admitted to the isolation ward received standard treatment according to the Chinese management guidelines for COVID-19 (version 6.0) . In brien = 154) were randomly selected and used as training dataset to establish a logistic regression model. Meanwhile, data obtained from the remaining 30% of the patients (n = 66) were used as test dataset to validate the effect of the model. Using the training dataset, continuous and categorical variables were presented as median with interquartile range (IQR) and total number (n) with percentage (%), respectively. The Mann–Whitney U test, χ2 test, and Fisher’s exact test were used accordingly to compare the differences between patients with and without ARDS. Univariate and multivariate logistic regression models were used to test the association between the risk factors and onset of ARDS. A receiver operating characteristic (ROC) curve was established to depict the predictive ability of the variables. The Youden index, which was calculated as the sum of the sensitivity and specificity minus 1, was used to determine the optimal cutoff value. The area under the curve (AUC) was calculated with the ROC curves to determine the differentiating abilities of the corresponding risk factors. Variables with significant differences between patients with and without ARDS were included in the multivariate logistic regression analysis, and a stepwise selection method was used to identify the variables included in the predictive model. The assigned risk score for the corresponding variables was determined by multiplying the β coefficients of significant variables by 10 and rounding off the value to the nearest integers, and the total risk score was calculated as the sum of those of the individual risk factors. In the verification dataset, the model was evaluated using the Hosmer–Lemeshow test and ROC curve. All data analyses were performed using SAS version 9.4 . Two-tailed P values < 0.05 for all tests were considered statistically significant. P values < 0.1 were used as the selection criteria for variables in the model.Data obtained from approximately 70% of the participants developed ARDS during hospitalization. Data about the characteristics of the study population collected upon admission and grouped according to the diagnosis of ARDS are presented in Table The median age of the patients was 57.5  years. The median age of the ARDS group was significantly higher than that of the non-ARDS group (69 [IQR: 62–74] vs 54 years [IQR 41–65] years). Approximately 53.90% of the patients were men. The male patients accounted for a larger proportion in the ARDS group (70.27%) than in the non-ARDS group (48.72%). About 13.64% of the patients were smokers, and the proportion of smokers was larger in the ARDS group (24.32%) than in the non-ARDS group (10.26%). The patients in the ARDS and non-ARDS groups did not differ significantly in terms of weight and clinical features upon admission. With respect to clinical features, fever was the most common symptom upon admission that was observed in 89.61% of the total population. The median time from symptoms onset to hospitalization in the ARDS group and non-ARDS group was 8 days and 10 days, respectively and there was no statistical difference. Moreover, patients with comorbidities were more likely to develop ARDS than patients without comorbidities (72.97% vs. 35.04%). The comorbidities included hypertension (43.24% vs 23.08%), diabetes (35.14% vs. 12.82%), coronary heart disease , and malignancies (10.81% vs. 1.71%). However, the difference was not significant in terms of the prevalence of CHD.In terms of the results of the laboratory examinations performed upon admission, COVID-19 patients with ARDS had significantly higher levels of white blood cell , alanine transaminase , aspartate aminotransferase , lactate dehydrogenase , creatinine , blood urea nitrogen , creatine kinase-MB , highly sensitive troponin I , prothrombin time , D-dimer , CRP , ESR , PCT , and ferritin  than COVID-19 patients without ARDS. Moreover, COVID-19 patients with ARDS had lower levels of lymphocytes  and serum albumin  than COVID-19 patients without ARDS. However, there was no significant difference between the ARDS and non-ARDS groups in terms of consolidation and bilateral pulmonary infiltration on chest CT scans.Regarding other important indicators such as PaO2 and PaO2/FiO2 at admission, We only recorded the above indicators for 29 patients, of which 28 patients were ARDS patients and 1 was non-ARDS patient. The median PaO2 of ARDS patients is 64 mmHg, PaO2/FiO2 is 101 mmHg.the Non-ARDS patient is 81 mmHg and 245 mmHg, respectively. Although the ARDS group has worse indicators, the data is insufficient, resulting in data differences between the two groups that are not comparable.The mortality in the ARDS group was 40.54%, while the mortality in the non-ARDS group was much lower than that of the ARDS group, which was 0.85%. This indicates that the ARDS group has a higher risk of death, and also shows the importance of the risk assessment model, which provides a basis for early detection and early treatment.Table The results for the association between differentially distributed risk factors and the risk of ARDS are shown in Table P value for the Hosmer–Lemeshow test was 0.312, indicating that the model had a good fit . The This is a retrospective study of patients diagnosed with COVID-19 and admitted to Wuhan Union Hospital. By comparing patients with and without ARDS, a panel of risk factors were identified. The univariate logistic regression model was used to assess the risk of individual factors, and the multivariate logistic regression model was utilized to identify the factors for the risk prediction model, which include LDH, BUN, D-dimer, PCT, and serum ferritin levels. Moreover, by assigning a risk score for the significant factors and calculating the total risk score, COVID-19 patients with a high risk of ARDS during hospitalization could be identified and this risk evaluation system had a good predictive efficiency.Among the variables included in the prediction model, the D-dimer level was assigned with the largest risk score at 34. Although the underlying mechanism of COVID-19 is still unknown, patients with this condition have an increased risk of thrombosis (preliminary data not shown). Bronchoscopy showed red jelly-like sputum, and biopsy of the lung tissues revealed disseminated hemorrhage in the pulmonary alveoli and clot formation within the microvessels . This reIn previous studies, an elevated ferritin level was considered a risk factor for the severity of different types of infection –23. AlthARDS is characterized by acute, diffuse, inflammatory lung injury; and inflammatory markers, including CRP, ESR, PCT, and serum ferritin, may worsen the clinical symptoms , 28. In Since SARS-CoV2 attack pulmonary epithelial cells and there is a risk of bacterial infection, the release of intracellular LDH, which is considered a general index of cell injury, is bound to increase , 35. MorMoreover, the importance of BUN in predicting ARDS has not received special attention. As severe infection and tissue damage can increase the rate of protein degradation , patientThis study had several strengths. First, Wuhan Union Hospital is one of the first designated institutions for treating patients with COVID-19 in Wuhan. Thus, the participants constituted a representative sample of hospitalized patients. Moreover, this study had one of the largest populations with definite outcomes during hospitalization, thereby providing a strong evidence for depicting the risk factors of ARDS among patients with COVID-19. By contrast, after a systemic selection of possible risk factors, a panel of indices routinely tested in clinical settings were selected, and a risk evaluation score was designed with a relatively good predictive efficacy for practical use. However, this study also had several limitations. First, the interpretation of the results may be limited by the relatively small sample size. Second, studies involving external verification must be conducted to validate the efficacy of our model in predicting ARDS. Finally, Due to the heavy clinical work during the epidemic, some important indicators such as PaO2 and PaO2/FiO2 have not been recorded in time, so these indicators cannot be included in our research.Several variables were found to be differentially distributed between COVID-19 patients with and without ARDS. A rough stepwise selection method and a panel of risk factors, including LDH, BUN, D-dimer, PCT, and ferritin levels, were included in the prediction model, and a risk evaluation scoring system was established, with an optimal cutoff value > 35 and AUC of 0.967 (95% CI: 0.925–0.989). Moreover, the model had excellent discrimination and calibration during the internal validation, which is considered practical for clinicians. The novel risk scoring system may be a valuable tool for screening COVID-19 patients with a high risk of ARDS.Additional file 1: Table S1. Predictive efficiency of the ARDS risk score among hospitalized COVID-19 patients.Additional file 2: Figure S1. ROC curves of individual risk factors for predicting ARDS.


Aarskog–Scott syndrome is a genetically and clinically heterogeneous rare condition caused by a pathogenic variant in the FGD1 gene. A systematic review was carried out to analyse the prevalence of clinical manifestations found in patients, as well as to evaluate the genotype-phenotype correlation. The results obtained show that clinical findings of the craniofacial, orthopaedic, and genitourinary tract correspond to the highest scores of prevalence. The authors reclassified the primary, secondary, and additional criteria based on their prevalence. Furthermore, it was possible to observe, in accordance with previous reports, that the reported phenotypes do not present a direct relation to the underlying genotypes. Aarskog–Scott syndrome (AAS), also known as faciogenital dysplasia, is a rare X-linked syndrome with a recessive mode of inheritance OMIM #305400). The condition was first described by Aarskog in 1970 and then detailed by Scott in two different families with multiple affected males . Aarskog05400. Th∗300546) was first described by German [The responsibility of FGD1 gene variants in the pathogenesis of the X-linked form of AAS , appraising the quality of evidence available and analysing the possible genotype-phenotype correlation.This systematic review was designed following the concepts of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) . A preplThe selected studies were those that met the following inclusion criteria: articles published in English; case reports that presented a complete description of the patients; male patients only; and cases that had a diagnosis confirmed by genetic testing, showing some types of pathogenic variants in the FGD1 gene. Incomplete articles, book chapters, cases without genetic proof, systematic reviews, meta-analyses, and any articles that did not meet all of the eligibility criteria were excluded.Initially, a manual search was performed through the databases, and after applying the eligibility criteria, articles were selected for full reading. After checking each article, a list of those selected was created, separating them by title, author, and year of publication. The listing process was performed using Excel 2019 software .Articles found through manual search were compared with the cases listed in the Human Gene Mutation Database (HGMDⓇ) . ThroughAfter searching the databases and matching with the HGMDⓇ, the following information was taken from each study: phenotypic characteristics of each patient reported; age (when given); nationality (when given); and type of pathogenic variant and molecular constituents involved in the pathogenesis of variants, as shown in The findings were listed in a spreadsheet, showing the frequency of each phenotype by patient, as described by each author. The data were entered into two independent worksheets by two authors (L.S. and V.Z.). Once completed, they were cross-checked and analysed to search for differences, which if found were resolved by a third author (A.F.), who did a complete read-through of the relevant article. Finally, the database was meticulously checked again by the third author. The order of the listed phenotypes respects the criteria proposed in  and compCase reports and case series are uncontrolled study designs known for an increased risk of bias. The quality assessment of the studies included in this work was performed using a validated tool for systematic reviews of case reports/case series proposed in ,27–29. AOne hundred and sixty-eight articles were found in the PubMed database and 14 in other search databases (LILACS and SciELO). In the search performed in the Latindex library, no article was listed. In total, 182 articles were collected and, after excluding duplicates, 154 remained. Another selection was performed considering the title and abstract, in which 72 were excluded, leaving 82. With the application of the eligibility criteria, a further 18 articles were excluded and 64 were selected for full reading . By the n = 22) were selected because they presented a complete phenotypic description as well as genetic testing, which confirmed the diagnosis of Aarskog–Scott syndrome. The sum of cases found in the articles was 58. From the 22 articles, 19 described ages, with a total of 43 patients, in which the average age was 10.6 (SD: 11.5 years old). Regarding the genotypes, 52 different pathogenic variants were described among the 58 cases, in which 33 patients presented with a point pathogenic variant (missense or nonsense mutations), representing about 56% of the total. In addition, 14 small deletions, four small insertions, three gross deletions, three splicing, and one duplication were described.The articles chosen for analysis (n = 11), secondary (n = 10), and additional (n = 9) categories based on the criteria proposed in [n = 86). However, only the most prevalent are shown in A total of 116 phenotypes were detected, which are grouped in onal n =  categoriposed in ,15. PhenCraniofacial manifestations were the most frequently described in the literature, representing 38.8% of all reported phenotypes, comprising 27.2% of the primary criteria, 40% of the secondary criteria, 44.4% of the additional criteria, and 39.5% of the others. Among the primary criteria, hypertelorism had a prevalence of 94.8% and was the most frequent phenotype among the 116. In addition, anteverted nostrils/short nose stand out with 75.9%, while bottom lip fold corresponded to only 5.2%. The main secondary phenotypes were ptosis (58.6%), widow's peak (46.6%), dysplastic ears (41.4%), and downward slanting palpebral fissures (39.7%). Among the additional criteria for craniofacial manifestations, long philtrum was the most prevalent (55.2%), followed by frontal bossing (27.6%), midface hypoplasia (20.7%), and lastly dental malocclusions (5.2%) .The heterogeneity of the manifestations presented by patients with AAS is notorious, and uncommon phenotypes such as retrognathia (1.7%), preauricular tag (3.4%), and epicanthal fold (6.9%) are also described.Orthopaedic changes represented 18.1% of the 116 phenotypes, almost 50% less than craniofacial ones. Even with a lower frequency, orthopaedic findings represented 63.6% of the primary manifestations, with short stature the most prevalent in the group (82.8%), followed by brachydactyly/wide fingers (62.1%) and clinodactyly (43.1%). It is noteworthy that the “others” category has considerable variability; for example, it contains phenotypes such as metatarsus varus with a prevalence of 22.4%, while the prevalence of both coxa magna and arthrogryposis is just 1.7%.n = 30), these were isolated with insignificant frequencies, for example, agenesis of corpus callosum, omphalocele, and sleep disorder, which in each finding had a percentage of 1.7%. This behavior corroborates with the premise of heterogeneity of the phenotypic findings of AAS.The group with both neurological and ophthalmological manifestations comprised 25.8% of all phenotypic findings. Developmental delay was the most prevalent clinical finding, with 17.2%, followed by attention deficit and hyperactivity disorder at 10.3%. Apart from developmental delay, no other manifestation was listed as a primary, secondary, or additional criterion. The vast majority of the findings were listed in the other category and, despite having a relevant number of findings  and amblyopia (1.7%).Changes in the genitourinary system corresponded to only 8.6% of the total phenotypes. However, shawl scrotum, one of the primary criteria, had a prevalence of 79.3%, being the third most common criterion of all 116. Cryptorchidism is the only representative of this group among the secondary criteria, with a prevalence of 50%, while in the additional criteria, only hypospadias (1.7%) featured. In addition, reports of micropenis (3.4%), vesicoureteral reflux (1.7%), and hydrocele (1.7%) were recorded.Aarskog–Scott syndrome is an X-linked recessive genetic condition due to pathogenic variants in the FGD1 gene. AAS is also known as facio-digital-genital dysplasia . The nom∗227330, 100050). Based on the analysis of phenotypes and pathogenic variants involved in all cases included in this study, overall it was not possible to define a clear genotype-phenotype correlation. Nevertheless, we consider it important to highlight that five members of the same family with a c.1341G>A; p.(Trp447∗) variant all presented with myopathic changes and distal arthropathy [To the best of our knowledge, etiopathogenesis is exclusively related to pathogenic variants that affect the FGD1 gene. However, the reported high frequency of failures in the detection of variants has been attributed to the heterogeneity of clinical findings, which leads to the possible involvement of other genes, even with different transmission modalities variously reported in the past  or whole-exome sequencing (WES), and, therefore, retrospectively reported as AAS cases. Possibly, in the future, with the growth of data drawn through these approaches, we will have diagnoses of AAS with a poor phenotypic expression.In conclusion, it was not possible to observe any evidence of a genotype-phenotype causal relationship between the type and position of the pathogenic variants with clinical expression, as previously observed by other authors. Furthermore, the organisation of clinical findings segregated according to their prevalence becomes truly useful for medical practice and management of patients with rare genetic diseases possibly to be directed to genetic tests. The need for a multidisciplinary approach from diagnosis to therapeutic interventions is also emphasised since the clinical manifestations affect diverse systems. Based on the exposed premises, the elaboration of more studies that seek to analyse the nature of clinical findings in light of a better understanding of the molecular mechanisms involved is suggested.


The geographic distribution of sporotrichosis in the United States is largely unknown. In a large commercial health insurance database, sporotrichosis was rare but most frequently occurred in southern and south-central states. Knowledge about where sporotrichosis is most likely to occur is essential for increasing clinician awareness of this rare fungal disease. Sporothrix. The infection typically follows cutaneous inoculation and involves the skin, subcutaneous tissue, and lymph nodes; pulmonary or disseminated disease occurs less frequently and usually affects immunocompromised persons  to examine the geographic distribution of sporotrichosis in the United States. These databases comprise health insurance claims data from outpatient visits, prescriptions, and hospitalizations for employees, dependents, and retirees throughout the United States. In 2018, the databases contained records for »27 million persons. MarketScan data are fully de-identified; thus, the Centers for Disease Control and Prevention institutional review board did not need to approve this study.We used the MarketScan Research Databases , a web-based platform, that comprises data from persons with health insurance plans that contributed prescription drug information to the MarketScan databases. We used data from February 1, 2012–December 31, 2018, to identify sporotrichosis patients using code 117.1 from the International Classification of Diseases (ICD), Ninth Revision, Clinical Modification and code B42 from the ICD, 10th Revision, Clinical Modification (ICD-10-CM). We used the primary beneficiary’s state of residence to calculate average annual state-specific rates per 1 million MarketScan enrollees. We evaluated underlying conditions on or in the month before sporotrichosis diagnosis, demographic features, and type of sporotrichosis. Of ≈76 million unique patients during 2012–2018, 1,322 had a sporotrichosis diagnosis code. For 1,236 (93.5%) of those, information was available about state of residence. The average annual rate of sporotrichosis cases per 1 million enrollees was highest in Oklahoma (6.1), Michigan (3.9), Kansas (3.5), and Kentucky (3.5) . NationwFor the 1,252 patients continuously enrolled during the month before their diagnosis, median age was 54 years; most (62%) patients were female . The mosAlthough sporotrichosis occurred rarely in this large sample of privately insured patients, it was most common in the southern and south-central United States. The US geographic distribution of sporotrichosis has not been well described since the 1940s, when most cases were observed in the Mississippi River basin, with the highest frequencies in North Dakota, Nebraska, Wisconsin, Kansas, and Missouri . However, MarketScan is one of the few data sources large enough to provide a sufficient number of sporotrichosis cases to analyze at a subregional level.Our findings are subject to several limitations. The primary limitation was that patients’ states of residence might not represent the exposure location or the original environmental source of S. brasiliensis (Knowledge about where sporotrichosis is most likely to occur is essential for increasing clinician awareness of this rare disease. Increased awareness might lead to faster diagnosis and treatment with antifungal medication, which most sporotrichosis patients need (


To support physicians in clinical decision process on patients affected by Coronavirus Disease 2019 (COVID-19) in areas with a low vaccination rate, we devised and evaluated the performances of several machine learning (ML) classifiers fed with readily available clinical and laboratory data. Our observational retrospective study collected data from a cohort of 779 COVID-19 patients presenting to three hospitals of the Lazio-Abruzzo area . Based on a different selection of clinical and respiratory (ROX index and PaO2/FiO2 ratio) variables, we devised an AI-driven tool to predict safe discharge from ED, disease severity and mortality during hospitalization. To predict safe discharge our best classifier is an RF integrated with ROX index that reached AUC of 0.96. To predict disease severity the best classifier was an RF integrated with ROX index that reached an AUC of 0.91. For mortality prediction the best classifier was an RF integrated with ROX index, that reached an AUC of 0.91. The results obtained thanks to our algorithms are consistent with the scientific literature an accomplish significant performances to forecast safe discharge from ED and severe clinical course of COVID-19. As of JAn early clinical prediction of clinical outcome could be beneficial for the patient’s management and for the optimal allocation of healthcare resources , 12. As We performed an overview of several state-of-the-art ML classifiers that, starting from routine clinical and laboratory analyses, could support physician’s decision making in achieving a safe discharge, as well as in forecasting disease severity and mortality risk of SARS-CoV-2 infected patients. All the ML classifiers, used in the study, were compared with the indexes to assess the risk of severe disease or death, commonly used in ED.The work presented in this paper is part of a larger research project whose aim was to design, develop and test a decision support system, called C19DSS (COVID-19 Decision Support System), based on ML predictors, that should assist physicians in clinical decision making [22.1This study retrospectively analyzed a cohort of SARS-CoV-2 infected patients presenting to ED, to collect a dataset for devising the ML models. Biochemical and clinical data of 779 adults (age > 18 years), affected by SARS-CoV-2, admitted during the first two waves to three hospitals of Lazio-Abruzzo area, were collected and analyzed. Missing data were managed by automatic imputation as described in the section below.Demographic, clinical, and biochemical variables were collected by a physician, during the first presentation at each ED.2/FiO2 ratio > 274, PaO2 > 65 mmHg, no oxygen supply during hospitalization, length of hospitalization < 10 days and no adverse events during hospitalization . The cut-off of PaO2/FiO2 ratio > 274 was chosen considering the results reported in our previous manuscript [The clinical outcomes were safe discharge, disease severity at seventh day of hospitalization and mortality during hospitalization. Data were collected at three different times: at ED presentation (T0), at the seventh day of hospitalization (T7) and at hospital discharge (TD). The disease severity was interpreted by following the interim guidance released by the WHO . WHO ordnuscript . The safnuscript , 23.clinicaltrial.gov, protocol number NCT04825301. The study was conformed to the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement and should be considered a prediction model study type 2a [The study was approved by the Internal Review Board of our Institution  and registered on  type 2a .2.2To devise the models in case of missing data, the dataset was pre-processed through missing data imputation system , 26. In 2.32/FiO2 ratio have been included as continuous variables. Sex at birth, fever (or not) and cough (or not) have been considered as categorical variables.To devise the ML models, both continuous and categorical variables were used. Heart rate (HR), respiratory rate-oxygenation (ROX) index, alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine, C-reactive protein (CRP), lactic dehydrogenase (LDH), platelet count (PLT), white blood cells count (WBC), neutrophil count (NEU), lymphocyte count (LIN), eosinophil count (EOS), basophil count (BAS), neutrophil lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), international normalized ratio (INR), D-dimer (DD), standard blood gas analysis variables and PaO2.4Primary outcome was the prediction  of safe discharge; secondary outcomes were the prediction of disease severity at seventh day  and mortality during hospitalization .2.5The descriptive analyses consist of frequency tables, medians and IQRs. Data reported in Based on a different selection of predictors, several models with complete set of predictors and with set of predictors were devised, details are reported in The ML task for all outcomes is binary classification. The following different models were tried from the available data: decision tree (DT), random forest (RF) and gradient boosting machines (GBM) –30. For In addition, to improve the explainability of the models, the variables importance was calculated and presented as graphs .To compare the ML models  with the standard clinical practice, the qCSI and 4C MS indexes was dichotomized. The confusion matrices (with the same associated statistics of the ML models) were calculated and finally, the results were compared with the predictions of the corresponding ML models, i.e., severity and mortality, respectively.All statistical analyses and ML models discussed in the paper were calculated with RStudio 2022.07.2–576 running R4.2.1 for Linux, using the “caret” and “mice” packages , 34. Due33.13/mcl (IQR 160–265), of WBC was 6.27 109/L (IQR 4.60–9.24), of neutrophil count was normal 4.55 109/L/L (IQR 3.11–7.60), of lymphocytes count was 0.92 109/L/L (IQR 0.64–1.33). D-Dimer was tested in D-Dimer Unit (DDU), its median value was 0.81 mcg/mL DDU (IQR 0.47–1.50). Respiratory parameter as PaO2 had a median value of 70.2 mmHg (IQR 59–83), the PaO2/FiO2 ratio 308 (IQR 223–365) and the ROX index 20.4 (IQR 13–20.9). Patients with severe disease or deceased were elder and characterized by higher values of CRP, LDH, D-Dimer, and progressive lower values for the PaO2/FiO2 ratio and ROX index, compared to the safe discharge population.The final dataset included 779 patients; demographic and clinical characteristics and principal biochemical markers for the study population are reported in At presentation in ED, 72.6% of patients reported fever and 54.1% referred cough; the clinical spectrum of disease of the whole cohort is outlined in 3.2As previously described, based on a different selection of predictors we devised several models, the results of all the prediction models developed are reported in 2/FiO2 ratio models. 2/FiO2 ratio models.PaO For safe discharge, RF is the most accurate in almost all the statistics, except for sensitivity, where GBM performs slightly better. The results for disease severity showed that RF performed with the best AUC; conversely, GBM has slightly superior sensitivity and accuracy. Regarding mortality during hospitalization, GBM reached the best AUC and specificity, on the other hand, RF performed the best accuracy and sensitivity . During the fourth wave, the system was used by the physicians of different institutions and was applied to patients with infection by SARS-CoV-2. So far, the validation cohort is made up of 57 patients. The mortality risk prediction model showed an accuracy of 0.94 for both GBM and RF models, whereas the models about safe discharge returned an accuracy of 0.70 and 0.60 (RF and GBM respectively).The best prediction models were selected and integrated into a system; further details are described in the paragraph below  to enable physicians to effectively use the previous models. The system is made up of a smartphone app used by clinicians, and a server that provides the “intelligence” to the app.In short, the app is made up of four activities see . The firWhen the classification process is activated, the app opens an encrypted connection to the server, sends the laboratory data and the ID of the patient  to the classification service. Then, the server applies the correct model, depending on the request and on the received data, stores the received data for further analyses, and returns the classification results to the app. The activities briefly described above were designed, developed, tested and redesigned following the UCD methodology, i.e., physicians were involved from the very beginning phases of the design, and then we adapted and improved the design/implementation according to consecutive cycles of usability tests. Physicians reported that the application was easy ed intuitive to use; the process of data entry and classification did not hamper the normal ED work routine. More details on the system, its architecture and on the usability results can be found in a further papers of our research group .4This study was finalized to devise ML methods, fed by routine clinical and laboratory analyses, to be used by physicians to manage safe discharge or hospitalization of unvaccinated COVID-19 patients. In particular, the models aim at predicting three different clinical outcomes: safe discharge, severe disease, at the seventh day after medical presentation, and mortality during hospitalization.2/FiO2 ratio models used for the prediction of safe discharge reached an AUC of 0.962 and 0.932, respectively. For severity prediction a CatBoost was the best model and reached an AUC equal to 0.84, versus the 0.911 of ROX index model and 0.832 of PaO2/FiO2 ratio model. For mortality prediction, the best classifier was a Support Vector Machines (SVM) model that reached an AUC of 0.91, versus 0.864 of ROX index (RF) model and 0.915 of PaO2/FiO2 ratio (GBM) model [2/FiO2 ratio, where a standard RF model achieved better performances.To compare our results with the literature, we proceeded as follows. First, we selected the highest accurate models, coming from peer-reviewed papers, that make use only of clinical data for the classification of the disease without the support of CT or X-ray images –39. The M) model , 40, 41.2/FiO2 ratio model the ROX index model was equal and even slightly superior in all the statistics, and it reached the best performance in the safe discharge model  to be calculated [To our knowledge this is the first study that integrated the ROX index into a machine learning model for clinical prediction , 43. Comge model . Althouglculated , 45. Forlculated , 23.With respect to other models available in the literature, ours are tailored to the clinical and laboratory data that are routinely collected at the first medical presentation in ED.As expected, the performance in the external validation demonstrated a lower accuracy for both mortality and safe discharge prediction. The reason can be twofold. First, the external validation was conducted on a smaller dataset. Second, the patients of the external validation were enrolled on a second wave of SARS-CoV-2 pandemic respect to those of the internal validation. Accordingly, the evolution and changes of dominant SARS-CoV-2 variants, between the different waves, could have taken a place in this reduced accuracy .5The C19DSS quickly predicted the prognosis in unvaccinated patients with pneumonia due to SARS-CoV-2 infection and, to the best of our knowledge, is the first attempt to provide an accurate prediction of safe discharge from ED. As future work, given the rapid dynamic of the pandemic, our AI-driven prediction models need to be updated to maintain high performance. To this aim, we have programmed to update the models, so that they will take into account the patient’s immunization status against SARS-Cov-2 infection, so to improve prediction also in vaccinated, partially vaccinated and immunocompromised patients.6Besides the limited size of the dataset and the constraint of using only routine clinical and laboratory data to devise the models, the performances of our models are in line and even better than the best prediction models in scientific literature. Furthermore, the prediction models are devised from a cohort of unvaccinated patients, and therefore its applicability is limited to countries with limited access to vaccines. We look forward to subsequent large sample studies and external validations in undeveloped countries.7We proposed a novel AI-driven prediction model integrated with the ROX index to forecast safe discharge and mortality in unvaccinated COVID-19 patients.We conceive that, in the next future, integration of prediction models in emergency care routine could improve healthcare assistance and represent an opportunity to make the people safer.Supplementary Material DetailsClick here for additional data file.


Preoperative and postoperative diagnostics play an important role in ensuring the safety of patients with phakic intraocular lenses (pIOLs). The risk of endothelial cell loss can be addressed by regularly measuring the endothelial cell density using specular microscopy and considering the endothelial cell loss rate and the endothelial reserve in accordance with the patient’s age when deciding whether to explant a pIOL. The anterior chamber morphometrics, including the anterior chamber depth and the distance between the pIOL and the endothelium, measured using Scheimpflug tomography and anterior segment optical coherence tomography (AS-OCT), can help to assess the risk of the endothelial cell loss. In patients undergoing posterior chamber pIOL implantation, accurate prediction of the vault and its postoperative measurements using AS-OCT or Scheimpflug tomography are important when assessing the risk of anterior subcapsular cataract and secondary glaucoma. Novel approaches based on ultrasound biomicroscopy and AS-OCT have been proposed to increase the vault prediction accuracy and to identify eyes in which prediction errors are more likely. Careful patient selection and regular postoperative follow-up visits can reduce the complication risk and enable early intervention if a complication occurs. Throughout the past seven decades of phakic intraocular lens (pIOL) development, one of the main areas of focus has been reducing the risk of postoperative complications . This leA variety of postoperative complications can occur in patients with pIOLs, including pupil ovalization, uveitis, hyphema, pIOL dislocation, synechiae formation, dysphotopsia, pupillary block glaucoma, pigment dispersion, anterior subcapsular cataract formation, and endothelial damage ,4,5,6,7.The aim of this review is to discuss how the preoperative and postoperative diagnostic techniques can be utilized to reduce the complication risk in patients with pIOLs, in particular that of endothelial cell loss, cataract development, and glaucoma, which are among the main concerns with the currently available pIOL models .Knowledge of different pIOL designs is important in order to understand how to address the potential complications. In general, pIOLs can be classified as those implanted into the anterior chamber and into the posterior chamber . The forThere are two iris-fixated pIOL models available on the market: Artisan , which is also known as Verisyse ; and Artiflex (Ophtec B.V.), also known as Veriflex  . The ArtDue to their ease of implantation, many of the early intraocular lens (IOL) models were anterior chamber angle-supported lenses . The devPosterior chamber pIOLs are implanted between the iris and the crystalline lens, with the haptics positioned in the ciliary sulcus ,28. MostIn recent years, new posterior chamber pIOL models have been introduced, but their long-term outcomes are yet to be evaluated ,35. The The main issue with the first anterior chamber IOL designs was their high rate of corneal decompensation . DespiteThe underlying cause for the endothelial cell loss observed in patients with pIOLs is probably related to mechanical damage to the corneal endothelium. This is apparent when analyzing the cases of dislocated iris-fixated pIOLs ,39. MechWhile the conventional slip lamp examination is an essential part of the ophthalmological examination and helps to detect pathologies such as pIOL subluxation or corneal edema due to endothelial decompensation, additional diagnostic modalities are necessary in order to obtain a complete picture of the factors influencing endothelial cell loss in pIOL patients.As significant endothelial cell losses are occasionally observed in patients with pIOLs, it is essential to regularly monitor the endothelium of each patient ,10,40. IThe most common technique for assessing the corneal endothelium of patients with pIOLs is specular microscopy . It prov2, while the ECD value based on manual analysis was only 728 cells/mm2 [2, and a standard deviation between the repeated measurements of up to 84 cells/mm2 [There are several aspects to be aware of when measuring ECD using specular microscopy. In order to obtain a reliable ECD measurement, a quality image of the endothelium is necessary, ideally while maintaining well-focused and clearly identifiable cells throughout the whole image. Low-quality images may lead to incorrect recognition of cell boundaries, especially by the automated algorithms . As the ells/mm2 . It is tells/mm2 . The maxells/mm2 . In geneells/mm2 . The repells/mm2 . Studiesells/mm2 ,48,49. Iells/mm2 ,51.In addition to specular microscopy, the ECD can be measured using confocal microscopy . However2, and it physiologically decreases with age at the rate of 0.5–0.6% per year [2 is reached [The average central ECD in an adult is around 2500–3000 cells/mmper year ,47,54,55per year ,56. Endo reached ,57.2 [The concept of the endothelial reserve is essential to understanding the implications of endothelial cell loss in pIOL patients. In practice, one may consider it as the difference between the current ECD in a particular patient and the critical ECD of 400–700 cells/mm2 ,44. To e2 . In addi2 . While t2 ,62,63,642 ,65,66,672 ,67. Howe2 . No stat2 . These d2 .2, which would ensure a density of at least 1500 cells/mm2 at the age of 70 years. Meanwhile, the ECD required for a 35-year-old was >2800 cells/mm2 [2 is mentioned by some authors as the minimum acceptable ECD in patients with pIOLs and the point at which the cataract surgery can still be safely performed [2 to 1020 ± 369 cells/mm2 after the explantation of iris-fixated pIOLs—an ECD value which is sufficient to avoid immediate postoperative endothelial decompensation, but which could increase the risk of corneal decompensation later in life, such as after cataract surgery.The concept of the endothelial reserve should be considered when deciding whether a pIOL is safe to implant in a particular patient . Youngerells/mm2 . An ECD erformed ,59,66,69erformed . When deerformed . For exa2, it seems more appropriate to act as soon as an unacceptable endothelial cell loss rate is confirmed [Therefore, instead of delaying pIOL explantation until the ECD decreases to 1500 cells/mmonfirmed ,40. Due onfirmed ,40. Whenonfirmed . A youngonfirmed .In general, the endothelial cell losses in most patients with ICLs are low ,75,76,77Anterior segment imaging techniques can be used to assess the relationship between a pIOL and the ocular structures . One of 2 and an edge distance of 1.43 mm to reach the ECD of 1500 cells/mm2 [To prevent endothelial damage, arbitrary safety distances of 2.0 mm from the center of the pIOL to the endothelium and of 1.5 mm from the edge of the pIOL to the endothelium have been proposed ,90,95. Uells/mm2 .Although the model by Doors et al. to predict ECD decrease in pIOL patients based on the distance from the pIOL to the endothelium was published more than a decade ago, the practice of measuring of this distance did not gain widespread acceptance in real-world clinical settings. One of the reasons for this could be the equipment and time needed for such measurement, as it is performed manually . ProbablThe direct measurement of the distance between the pIOL and the corneal endothelium can only be performed postoperatively ,56. One Several studies have analyzed endothelial cell loss in patients with anterior chamber iris-fixated pIOLs in relation to the ACD ,19,20,99Until recently, it was not uncommon to implant iris-fixated pIOLs in patients with relatively shallow anterior chambers. For example, the average preoperative ACD, measured from the corneal epithelium, was 3.27 ± 0.31 mm across 293 eyes implanted with Artiflex Myopia pIOLs between 2004 and 2016 in an academic hospital .The relationship between the ACD and endothelial cell loss in posterior chamber pIOLs has not yet been clearly established. Niu et al. examined the outcomes of 51 eyes with shallow ACD measurements of <2.8 mm, measured from the endothelium, which were implanted with ICLs . AlthougWhen discussing the association between endothelial cell loss and anterior chamber morphometrics, it should be noted that these measurements change with age. Due to increasing crystalline lens thickness, the ACD decreases with age, which is also reflected in the continuously decreasing distance between the pIOL and the endothelium ,82,105. Cataract development is another potential complication in patients with pIOLs ,113,114.In contrast to endothelial cell loss, the higher risk of developing cataract is primarily observed in patients with posterior chamber pIOLs, while the anterior chamber pIOLs do not seem to significantly increase the risk ,113,114.The decreasing cataract rates in patients with newer ICL models may be attributed not only to the presence of the central port, but also to the increased distance between the ICL and the crystalline lens, also known as the vault . Using UPosterior chamber pIOL design changes reduced but did not eliminate the risk of developing lens opacities. In the newer ICL models with the central hole design, it appears that a low vault is less likely to cause cataracts. Gonzalez-Lopez et al. examined 24 eyes with low central vaults ranging from 9 µm to 94 µm (mean vault ± SD of 52 ± 19 µm) and found an anterior subcapsular cataract in only one eye (4.2%), after a mean ± SD follow-up of 5.8 ± 0.9 years . In thisIn order to reduce the risk of cataract, low vaults should be avoided, and the approaches to the ICL size selection and vault prediction are discussed later in this article. In addition, the vault should be monitored postoperatively, as it was found to decrease over time ,131. SchElevated intraocular pressure is occasionally observed in patients with pIOLs ,134,135.In rare cases, chronically elevated intraocular pressure can occur due to pigment dispersion caused by rubbing between the ICL and the iris . Ye et aMore commonly, chronically elevated intraocular pressure can occur due to the narrowing of the anterior chamber angle due to a higher vault . UsuallyThe findings of the published studies suggest that elevated intraocular pressure is a relatively rare complication in patients with posterior chamber pIOLs, but its rate may increase over a longer follow-up period. Although the reason for this is not yet clearly established, decreases in the anterior chamber angle due to increases in crystalline lens thickness over time might play an important role . In addiMuch like with the posterior chamber pIOLs, elevated intraocular pressure can occasionally occur due to an ophthalmic viscosurgical device remaining in the eye, steroid response, and pupillary block in patients with anterior chamber lenses ,138. To As for other pIOL complications, regular postoperative follow-up examinations are necessary, as a timely diagnosis and treatment is crucial to prevent irreversible vision loss. Intraocular pressure must be measured at each follow-up visit. The slit lamp examination should focus on signs of a narrow anterior chamber angle, pigment dispersion, chronic inflammation, synechiae, and the evaluation of the optic disc. Anterior segment imaging techniques can be used to evaluate the vault and anterior chamber morphometrics in order to assess the risk of developing ocular hypertension in patients with posterior chamber IOLs. Finally, OCT of the retinal nerve fiber layer and visual field examination can be helpful when detecting glaucomatous damage  135,140140.Methods of measuring the central vault include AS-OCT, Scheimpflug photography, and UBM, and these techniques were discussed above ,141,142.When measuring the vault, attention needs to be paid to the lighting conditions. The vault is not a static parameter and changes according to the pupil size ,147. In The vault can also be measured intraoperatively, using optical coherence tomography (OCT) integrated into the surgical microscope . AlthougAs suboptimal vaults are associated with an increased risk of complications, and the vaulting depends on the size of the ICL, selecting the correct size is necessary to ensure the safety of patients with these lenses ,137. TheUsing various preoperative parameters obtained via AS-OCT and Scheimpflug tomography, one study found the horizontal anterior chamber angle-to-angle distance and the crystalline lens rise , along with the ICL size, its spherical equivalent, and the patient age to be predictors of the vault size. Still, they could only explain 34% of vault variability . AnotherOne of the reasons for suboptimal vaults achieved in some eyes using the traditional method appears to be a poor correlation between the measured white-to-white distance and the sulcus-to-sulcus diameter, which better represents the actual position of the implanted ICL ,156,157.The sulcus-to-sulcus diameter and other ciliary body parameters are used in some of the alternative ICL size selection methods ,156,160.There is growing interest in developing a more accurate vault prediction method using AS-OCT measurements as these devices seem to be becoming increasingly common in ophthalmology clinics. Even though it cannot obtain ciliary body parameters, the AS-OCT is appealing because it allows a fast, repeatable, and non-contact measurement ,90. The Regardless of the accuracy of the method used, the limited choice of ICL sizes remains an issue which prevents achieving more consistent outcomes in terms of the vault . NakamurDesign modifications of pIOLs have significantly reduced but have not eliminated the risk of complications. The safety of patients with pIOLs can be further improved by utilizing preoperative and postoperative diagnostic techniques, such as specular microscopy, AS-OCT, Scheimpflug tomography, and UBM. Careful patient selection and regular postoperative follow-up are necessary in order to reduce the complication risk and to enable early intervention in case a complication occurs.


CPET and physical performance tests, including the agility t-test, vertical jump test, stork static balance test (SSBT), and dynamic Y-balance test (YBT), were performed. Spearman correlation (r) and p-values are reported. The postural parameters were found to have moderate-to-high associations with the CPET and agility test, moderate correlations with the vertical jump test and SSBT (head and pelvic postures only), and weak correlations with the YBT. As the postural parameters were more asymmetric, both the CPET and performance skills scores were worse. For example: (1) a medium positive correlation was found between cranio-vertebral angle (CVA) and the vertical jump test  and SSBT , while a strong negative correlation was found between CVA and the agility test . (2) A strong positive correlation was found between CVA and oxygen uptake efficiency slope, load watts VO2 at VT, VO2/kg, and load watts at the respiratory compensation point (RCP) . Conversely, a significant negative correlation was found between CVA and VE/VO2 at VT . Postural rotations and translations of the head, thorax, and pelvis were statistically correlated with the physical performance skills and CPET in the young collegiate athletes. There were moderate-to-high associations with cardiopulmonary functions and the agility tests, moderate correlations with the vertical jump test, and weak correlations with the YBT. Postural alignment may be important for optimal physical performance and optimal cardiopulmonary function. Further research is necessary to elucidate the reasons for these correlations found in our sample of young and healthy athletes.This study assessed the relationship between body posture displacements, cardiopulmonary exercise testing (CPET), and skill-related physical fitness tests. One hundred male (60%) and female collegiate athletes (22.2 ± 4 yrs) with normal body mass indexes (BMI up to 24.9) were assessed via the PostureScreen Mobile Athletic performance is influenced by various factors such as strength, endurance, and skill ,2,3. HowPostural aberrations can have a negative impact on both respiratory function and physical fitness components, which, in turn, can lead to decreased athletic performance and an increased risk of injury ,11,12. FIn 1980, Harrison detailed the possible postural displacements of the head, thorax, and pelvis as rotations and translations with 12 simple motions (6 possible rotations and 6 translations in each region) in 6 degrees of freedom ,18. ThisRecent technological advancements have made it possible to accurately measure upright posture as translational and rotational displacements of the head, thoracic cage, and pelvis ,26. ThesOne hundred healthy male and female collegiate athletes were recruited in this study. The inclusion criteria were: (i) ages between 17 to 26, and (ii) a normal body mass index (BMI) of up to 24.9. Ethical approval was obtained from the Ethics Committee of the University (REC-22-11-26-S) and informed consent was obtained from all the participants prior to the data collection, in accordance with the relevant guidelines and regulations. All the participants were screened for the following exclusion criteria: (i) inflammatory joint disease or other systemic pathologies; (ii) a prior history of overt injury and surgery relating to the musculoskeletal system or a disorder related to the spine and extremities; and (iii) musculoskeletal pain in the previous three months.® Mobile app (PSM). The PSM application has been shown to be a reliable and valid method for evaluating static posture [The postural examinations were carried out with a photographic method using the PostureScreen posture ,26. PSM Cardiopulmonary exercise testing (CPET) performance analyses were conducted within the recommendations of the American Heart Association (AHA) ,28. All All the performance data, including blood pressure, were recorded continuously during the tests. For the ventilatory parameters, we considered the VE/VCO2 slope (ventilatory efficiency), VO2 (mL/min) at VT , VO2/kg , and VE I/min (VE/VO2 at VT)  . All the2) measurement, calibrating the gas analyzers, flow sensor zeroing, and flow calibration. We started the protocol with a single minute sitting/resting phase, automatically followed by a 2 min flat walk at 6 km/h as a warm-up setting. After the 2 min flat walk phase, the treadmill speed was increased automatically by continuous 8 and up to 10 km/h inclined running, with an increasing slope of 1.0% at every minute until participant exhaustion was recorded. This protocol was pre-set before the start of the test. After termination of the running (VT), measurements were continuously taken for 1 min at a 4 km/h walk, followed by a 4 min rest [A maximal CPET protocol was carried out on the treadmill  with sport-specific incremental protocols. Calibration was carried out before the start of the tests and repeated as needed during the day. The calibration included checking for the environmental carbon dioxide  . The parThree trials were conducted in each direction with a rest interval for 2 min and the best out of these three trials was used. The test–retest reliability (ICC’s) for the different reach directions ranged between 0.90 and 0.95 .A statistical analysis was then performed using SPSS statistical software version 25. A Fisher Z transformation was utilized for a sample size estimation with the power set at 0.80, the beta set at 0.20, and the alpha set at 0.05. The estimated needed sample size for correlation was at least 74. A larger sample size of 100 was used to ensure external validity and strengthen the study.p-values are reported to demonstrate the relationship between the posture parameters and physical performance skills, as well as the posture parameters and cardiopulmonary functions. The correlation coefficient can be any number between −1 and 1, where a negative sign indicates a negative correlation and a positive sign indicates a positive correlation. In terms of the association, for absolute values of r, 0–0.19 was regarded as very weak, 0.2–0.39 as weak, 0.40–0.59 as moderate, 0.6–0.79 as strong, and 0.8–1 as a very strong correlation. The level of significance was set at 0.05 and the correlation was considered to be statistically significant when the p-value was less than 0.05.Descriptive data are presented as mean ± standard deviation. To determine the normality of the collected numerical variables, the Shapiro–Wilk test was used; however, none of the numerical variables satisfied the parametric assumptions, so they are described using the median and interquartile range (IQR). Next, a spearman correlation was performed, and the correlation coefficient (r) and The participant characteristics are shown in p-value < 0.001), while a strong negative correlation was found between CVA and the agility test . A medium positive correlation was also identified between CVA and the stork balance test . A negative medium correlation was found between the stork balance test and lateral head translation, as well as anterior head translation (AHT) . There was, however, a very weak correlation between the stork balance test and lateral angulation head, and a weak correlation with thoracic kyphosis .Several correlations were identified between the posture parameters and physical performance skills . A mediup-value = 0.004, <0.001, <0.001, respectively), and a strong positive correlation between lateral translation head sagittal and the agility test . A negative medium correlation was found between anterior head translation (AHT) coronal and the y-balance test anterior left, y-balance test posterolateral left and right, and y-balance posteromedial left and right , and a strong positive correlation was found between AHT coronal and the agility test . Additionally, a strong positive correlation was found between lateral angulation head and the agility test , thoracic kyphosis and the agility test , and rib anterior translation coronal and the agility test .There was also a negative medium correlation between lateral translation head sagittal and the y-balance test anterior left and y-balance test posterolateral left and right , while a significant positive strong correlation was found between pelvic tilt and the agility test . There was, however, a weak negative correlation between pelvic tilt and the stork balance test . A negative medium correlation was found between hip pelvis lateral angulation and the vertical jump test, as well as with the stork balance test , and a positive strong correlation was found between hip pelvis lateral angulation and the agility test . Similarly, there was a medium negative correlation between hip pelvis anterior translation coronal and the vertical jump test, as well as with the stork balance test , and a positive strong correlation was found between hip pelvis anterior translation coronal and the agility test . Lastly, there was a medium negative correlation between hip pelvis lateral translation and the vertical jump test , while a significant positive strong correlation was found between hip pelvis lateral translation and the agility test .Furthermore, a medium negative correlation was found between pelvic tilt and the vertical jump test . Conversely, a significant negative correlation was found between CVA and VE/VO2 at VT . Furthermore, there was also a strong negative correlation between lateral translation head sagittal and oxygen uptake efficiency slope, load watts VO2 at VT and VO2/kg, and load watts at RCP; , and a strong positive correlation between lateral head translation and VE/VO2 at VT .There were also significant correlations between the posture parameters and cardiopulmonary functional parameters identified . A stronThis investigation sought the association between postural displacements such as translations and rotations of the head, thorax, and pelvis in relation to both cardiopulmonary exercise tests and physical performance skills. Our hypothesis that multiple (more than one plane and more than one region) postural displacements would impact and alter a specific set of athletic skills and cardiopulmonary measures was supported by our primary findings. In our young collegiate athlete populations, we identified many statistically significant correlations between posture displacements of the head, thoracic cage, and pelvis and their cardiopulmonary and athletic skills, including moderate-to-high associations with cardiopulmonary function measures and agility testing, moderate correlations with the vertical jump test and stork balance test, and weak correlations with the YBT. The differences in both the cardiopulmonary and physical performance skills based on the postural parameters in this young and healthy population add to the growing body of literature showing the importance of posture; that is, a poor postural alignment has been associated with negative effects in asymptomatic populations ,13. SpecA recent study assessing sensorimotor integration and somatosensory processing variables between asymptomatic individuals with and without forward head posture found that the CVA was significantly correlated with all the measured neurophysiological variables, indicating that, as forward head posture increased, the sensorimotor integration and somatosensory evoked potential (SEP) processing became less efficient . RegardiThe question arises of how asymmetry in posture, in terms of rotations and translations, contributes to decrements in physical performance skills? First, any asymmetric posture automatically changes the three-dimensional length-tension relationships of the muscles and tendons , and becMany studies have found differences between athletes and non-athletes in body posture ,43, as wp < 0.001. Posture as assessed as translations and rotations of the head, thorax, and pelvis has not, to our knowledge, previously been correlated with cardiopulmonary functional measures, and it is not known how posture and cardiopulmonary measures are related. We speculate, however, that one of the mechanisms responsible for some of the main effects has to do with how posture alterations in the head and cervical spine influence respiratory mechanics [The cardiopulmonary exercise tests, including a greater VO2 at VT, greater VO2/kg, and smaller VE L/min, all demonstrated poorer values and were statistically significantly correlated to asymmetric postures for all the posture parameters assessed , thoracic spine (increased kyphosis and anterior translation), and pelvis  that have an influence on athletic skills and cardiopulmonary exercise tests. These specific postures should be considered in future interventional trials as outcome assessments that might influence human performance. Further research is necessary to elucidate the reasons for the correlations found in our sample of young and healthy athletes.


Despite the existence of modern antidiabetic medications, diabetes still affects millions of individuals worldwide, with a high death and disability rate. There has been a concerted search for alternative natural medicinal agents; luteolin (LUT), a polyphenolic molecule, might be a good choice, both because of its efficacy and because of it having fewer side effects, compared to conventional medicines. This study aims to explore the antidiabetic potential of LUT in diabetic rats, induced by streptozotocin , intraperitoneally. The level of blood glucose, oral glucose tolerance test (OGTT), body weight, glycated hemoglobin A1c (HbA1c), lipidemic status, antioxidant enzymes, and cytokines were assessed. Also, its action mechanism was explored through molecular docking and molecular dynamics simulations. Oral supplementation of LUT for 21 days resulted in a significant decrease in the blood glucose, oxidative stress, and proinflammatory cytokine levels, and modulated the hyperlipidemia profile. LUT also ameliorated the tested biomarkers of liver and kidney function. In addition, LUT markedly reversed the damage to the pancreas, liver, and kidney cells. Moreover, molecular docking and molecular dynamics simulations revealed excellent antidiabetic behavior of LUT. In conclusion, the current investigation revealed that LUT possesses antidiabetic activity, through the reversing of hyperlipidemia, oxidative stress, and proinflammatory status in diabetic groups. Therefore, LUT might be a good remedy for the management or treatment of diabetes. Diabetes mellitus (DM) has emerged as a major health issue in recent years, and the complications associated with it are receiving high attention. DM is associated with a deterioration in life expectancy and a worsening in the quality of life. According to the reports of the International Diabetes Federation, the number of adults living with diabetes is 537 million, and estimated to reach 783 million globally, and 124.87 million in India, by 2045 [Despite the widespread use of oral antidiabetic agents, diabetic complications induced by metabolic alteration, oxidative stress, and inflammation still occur, requiring adjuvant therapies. Immunosugars are the chemical compounds in which nitrogen molecules from the ring have been substituted with oxygen molecules; different immunosugars have been investigated to manage hyperglycemia, for effective management of this ever increasing disorder. These immunosugars were found to reduce intestinal glucose absorption by inhibiting α-1,4-glucosidases enzyme . AlthougIn order to provide novel, effective medications, with fewer side effects, the pursuit of antidiabetic medicines has been directed to medicinal plants . Plant-dLUT has been shown to have a variety of bioactivities including anti-inflammatory, and antioxidant activity, which may be useful in the treatment of various chronic diseases associated with oxo-inflammations such as cardiovascular disease, liver disease, etc. .Moreover, it also modulates various signaling pathways, gene expression, and cellular metabolism. This makes it a potentially multifunctional molecule that might be used to target many bodily pathways and processes. Besides that, LUT is largely regarded as a safe molecule because it is derived from natural sources .The use of molecular docking and molecular dynamics simulations is a newly developed interdisciplinary approach for high throughput screenings of biomolecules . The molOverall, the potential health benefits, natural origin, mechanisms of action, and safety profile of LUT make it a promising candidate for further research; therefore, by using this unique approach, we want to explore the potential of LUT as a safe and effective drug for the management of diabetes.The IC50 value of LUT showed potential inhibitory effects on α-glucosidase in a dose-dependent manner. For glucosidase, the IC50 value was observed at 41.22 ± 1.18. As a result, inhibiting α-glucosidase is a very effective approach to delaying glucose absorption and lowering postprandial blood glucose levels, perhaps slowing diabetes progression.Hyperglycemia is the most important symptom of diabetes. The OGTT is an important indicator of diabetes alleviation. In the OGTT, the plasma glucose level was taken at 60 and 120 min; a significant reduction in plasma glucose level (glucose load) was observed in the treatment group Effect of LUT treatment on blood glucose levels in diabetic rats.Diabetic rats exhibited a significant increase in the levels of blood glucose as compared to the NC group. After administration of LUT to diabetic rats, their blood glucose levels were significantly reduced. The glibenclamide (GLB)-treated group showed similar results to those treated with LUT. The overall results are presented in The observed data reveal that LUT treatment might reverse glucose levels in diabetic rats.The glycated Hb level was evaluated to check the efficacy of the test drug for its long-term effect on glucose regulation. As compared to the control group, the HbA1c level was significantly increased in the diabetic group c. After As shown in Body weight, and different biochemical parameters investigated in this study, revealed the overall level of triglycerides (TG), total cholesterol (TC), high-density lipoproteins (HDL), and low-density lipoproteins (LDL) in the NC, DC, and treatment groups. In the DC group, the levels of TC, TG, and LDL increased significantly, while lower levels of HDL were observed. After oral administration of LUT and GLB, the levels of TC, TG, LDL, and HDL were significantly reduced, returning to near normal levels. The LUT treatment showed a similar ameliorative effect to GLB. Overall, these results indicate that LUT supplementation could improve the hyperlipidemic condition in diabetic rats .As compared to the NC group, significant increases in the alanine transaminase (ALT), aspartate transaminase (AST), and alkaline phosphatase (ALP) levels were observed in the DC group. After treatment with LUT, a remarkable reduction in the levels of ALT, AST, and ALP was observed. After 21 days of treatment with LUT and GLB, the levels of these markers was nearer to the values of the NC group .LUT and GLB showed similar results, except for ALT. The results suggest that LUT could modulate liver function.The results of kidney function tests of all the groups are shown in The results of SOD, CAT, and GSH expression in the groups are presented in The expression of tumor necrosis factor-α (TNF-α) in all the groups was analysed using ELISA. Histological observation of pancreatic cells showed circumscribed masses surrounded by deeply stained pancreatic exocrine cells, and β-cells are identified in the NC group. In the DC group, β-cells degenerated and appeared disorganized, as shown in In the treatment group, the population of β-cells and pancreatic tissues were significantly restored, near to normal, as evidenced by the histological observation. The histological observation of the liver section of the STZ-induced DC group showed congestion of hepatocellular necrosis, and central veins with vacuolization, compared to the NC group .However, after treatment with LUT and GLB, a normal hepatic architecture was attained by reversing hepatic necrosis, cell vacuolization, and central veins. Histological observation of the kidney cells revealed that the NC group displayed normal glomerulus and Bowman’s capsules, while in the DC group, abnormal glomerular architecture and dilated Bowman’s capsules were observed. LUT and GLB treatment returned the glomerulus to normal and regulated the Bowman’s capsules .To explore the possible binding interactions between LUT and the antidiabetic receptor glucosidase, we carried out molecular docking studies of the LUT into the native ligand binding regions of the α-glucosidase receptor (PDB: 3W37) Docking scores, binding energies, and the types of interaction of LUT with α-glucosidase are presented. As shown in The LUT compound showed a docking score of −7.701 kcal/mol and glide energy of −54.698 kcal/mol . These tThe root-mean-square deviation (RMSD) of the MD simulation trajectories system was employed to examine the dynamic profile of the α-glucosidase receptors. The RMSD suggest that docked ligand complex systems attained equilibrium within a time interval of 10,000 picoseconds (ps), and remained stable throughout the MD simulation. The ligands’ RMSD plot indicates that acarbose exhibits lower RMSD values than LUT a,b.The MD simulation shows different types of bonding between ligand receptors, 2D ligand receptors and the percentage of amino acid interaction are shown in Two-dimensional ligand–protein interactions in the molecular dynamics simulation revealed that acarbose and LUT form hydrogen bonds, hydrophobic contacts, and water bridges. In both acarbose and LUT, the hydrogen bonds predominantly bind with different amino acids, to attain equilibrium state. For each ligand, the RMSD of the ligand with respect to the protein backbone was further examined; the RMSDs of the 3W37 complex with LUT and acarbose were 4.8 ± 1.1 and 1.1 ± 0.4, respectively.LUT is a polyphenol used to treat various diseases and disorders by influencing oxidative stress, inflammation, and dyslipidemia, and slowing carbohydrate digestion and absorption by interacting with α-glucosidase ,16. In tPreviously published reports demonstrated that loss in body weight is a typical characteristic of DM, which was evidently shown in our experiment . Body weBlood glucose values and OGTT are important indicators, and are considered key symptoms of DM . In thisLipidemic dysfunction is typically linked with DM, and plays a crucial role in the development of cardiometabolic disorders, hence, as a result of lipidemic malfunction, diabetic patients are more prone to cardiovascular diseases. Similarly, in the current investigation, a close relation between hyperglycemia and dyslipidemia was recorded . This isBoth the liver and kidneys are important organs in the body, that play a crucial role in regulating many physiological processes. Damage to these organs, either by oxidative stress, inflammation, or hyperlipidemia, can have grave consequences for human health . The livHumans have an effective defense mechanism against free-radical-induced damage, using antioxidant enzymes like SOD, CAT, and GSH. Oxidative stress  can lead to diabetes as a chronic ailment ,31,32. INotably, the previously reported literature has suggested that abnormal cytokine (TNF-α and IL-6) levels in the blood are associated with pancreatic β-cell death and dysfunction ,33. In tThe in silico molecular studies of LUT’s interactions with the active sites of the enzyme (glucosidase), showed excellent inhibition of enzyme activity. The interactions between proteins and ligands showed strong hydrogen bonding. These confirm that LUT can be used as an antidiabetic candidate . PolypheLuteolin (LUT) showed an excellent antidiabetic effect in the present investigation and it is very similar to the standard, GLB. Therefore, LUT could be deemed as a good candidate, in the future, for the management or treatment of diabetes. Although this work reports on the antidiabetic activity of LUT, systematic investigations should be carried out both on its effects and mechanism, as well as its toxicity, to establish its actual role in diabetes.The commercial kits for the measurements of TC, TG, low-density lipoprotein LDL, high-density lipoprotein HDL, TNF-α, IL-6, and HbA1c were purchased from Sigma-Aldrich. Luteolin 97% was procured from Almanac Life Science India Pvt. Ltd., New Delhi, India. All the reagents were of analytical grade.In vitro α-glucosidase activity was evaluated according to the defined protocol ,37. DiffWistar albino rats, weighing about 150 g, were acquired from the Central Animal House at Jamia Hamdard in New Delhi, India. The animal procedures were performed as per CCSEA guidelines. According to the design protocol, animals were divided into different groups (n = 6). A 12 h cycle of light and dark was maintained at 25 ± 2 °C. Experimental animals were familiarized for a week prior to starting the experimentation, and normal chow was given.A total of 24 animals were taken in this study, all the animals were divided into four groups. Group 1, received the vehicle, group 2 received a toxicant (STZ 50 mg/kg), group 3 received LUT , and group 4 received glibenclamide . After an STZ injection, fasting blood glucose levels were measured. Only animals having blood glucose levels higher than 250 mg/dL were included in the study. Blood was collected from the retro-orbital plexus. Animals were sacrificed and disposed of by biotic waste management, New Delhi, as per CCSEA guidelines .w/v glucose solution). Blood sugar levels were measured using a glucometer [A test for OGTT was performed at an early stage in the experiment. The level of blood glucose 1 was measured at 0, 30, 60, and 120 min after oral administration of glucose  was measured in all treatment groups according to the manufacturer’s protocol using a span diagnostic kit.Body weight was measured on day 0, 7, 14, and 21.g for 10 min and stored at −80 °C until use. Serum glucose level, liver function parameters , kidney function parameters , and lipid profiles  were assayed [The rats were anaesthetized and blood was collected from the eyeball of the rat. Further, serum was recovered through centrifugation at 3000×  assayed .Quantification of the levels of TNF-α and IL-6 expression in the serum of all the groups of rats under study was performed by ELISA kit. The obtained results were expressed as pg/mL .After the experiment was completed, a piece of the liver was homogenized and processed in PBS to determine SOD, CAT, and GSH activity/levels .For histological analysis, 10% formalin was utilized as a fixative and paraffin was used to embed cells from the pancreas, liver, and kidneys from experimental animals. Hematoxylin and eosin (H&E) staining was performed on tissue slices cut at a thickness of 5 mm staining agent ,32.Our docking study was conducted using Maestro Schrodinger (version 11.8) to determine the modes of interaction of LUT in the active sites of α-glucosidase . We seleThe Desmond tool, from Schrodinger, was used to run MD simulations to obtain an insight into the binding stabilization of the receptor α-glucosidase’s (PDB: 3W37) native structure and in docked complexes with LUT and acarbose. Before the MD simulations, the hydrogen bond network in both the native and docked complex structures were refined at pH, employing the Protein Preparation Wizard from Schrodinger, and the final restrained minimization was carried out using the OPLS3e force field . An orthp values < 0.05.Data were examined using Prism in Graph Pad. All the results are displayed as the mean ± SD (standard deviation). Statistics were considered significant for LUT, a naturally occurring polyphenolic, has been shown to possess potential health benefits for the management of diabetes. The current investigation revealed that LUT can reduce blood glucose levels, lipid malfunction, and body weight in diabetic rats, and also delay carbohydrate digestion and absorption. Moreover, the reversal of oxidative stress and inflammatory status confirms its antioxidant and anti-inflammatory effects. Further, the in vivo results were supported by molecular modelling and simulations, molecular docking with the 3W37 protein was shown to have comparable results to the innate ligand acarbose. In conclusion, LUT represents a potentially effective alternative for the development of novel therapeutics to manage diabetes, and may enhance the quality of life for those who already have the disease. However, this investigation needs extensive investigation on a molecular level to confirm its antidiabetic potential.