Objective To explore the efficacy and safety of chemotherapy combined with immunotherapy as the first-line treatment of advanced or metastatic squamous NSCLC.Methods Two researchers independently searched PubMed, the Cochrane Library, EMBASE, CNKI, Wanfang Data, and other databases by using a computer, collected the clinical trials or randomized controlled trials published by April 2022 about immunotherapy combined with chemotherapy as the first-line treatment of advanced or metastatic squamous NSCLC, screened the literature, and extracted the data according to the nanodischarge criteria. We used Revman5.4 for statistical analysis of the included studies, and publication bias was analyzed with Egger’s test in Stata12.Results A total of seven clinical trials were included, including 1,510 cases in the chemotherapy combined with the immunotherapy group and 1,519 cases in the chemotherapy group. In terms of effectiveness, compared with the chemotherapy group, chemotherapy combined with immunotherapy for advanced or metastatic squamous NSCLC had longer overall survival  and progression-free survival . In terms of safety, the chemotherapy combined with immunotherapy group has a higher risk of adverse reactions at any level and above three levels of hematotoxicity, gastrointestinal abnormalities, and liver dysfunction than the chemotherapy group. Egger’s test has minor publication bias.Conclusion Chemotherapy combined with immunotherapy is effective as the first-line treatment for advanced or metastatic squamous NSCLC, but the risk of adverse reactions is relatively high. If there are adverse reactions in clinical application, it should be treated in time. Clinical Trial Registration:https://Systematicreview.gov/, identifier [registration number]At present, the mortality rate of lung cancer is still the highest among malignant cancers in China, and the 5-year survival rate is about 20.5% . Lung caTumor immunotherapy is to control and kill tumor cells by mobilizing the function of the body’s immune system and enhancing antitumor immunity. It is the most effective antitumor treatment after surgery, radiotherapy, chemotherapy, and targeted therapy . In receWe systematically searched for global clinical trials or RCTs of chemotherapy combined with immunotherapy as the first-line treatment of advanced or metastatic squamous NSCLC and systematically evaluated its efficacy and safety.Inclusion criteria: 1) included population: patients with advanced or metastatic sq-NSCLC who received chemotherapy combined with immunotherapy; 2) literature: retrospective study and prospective study; 3) intervention measures: the treatment group was given chemotherapy combined with immunotherapy , and the control group was given placebo combined with chemotherapy or conventional chemotherapy; 4) OUTCOME MEASURES: the main outcome measures included progression-free survival (PFS) and overall survival (OS)–related hazard ratio (HRS) and 95% confidence interval (95% CI). Secondary outcome measures included adverse reactions at any level  and adverse reactions above level 3 .Exclusion criteria: 1) no control group was established; 2) repeatability study; 3) non-Chinese and English literature; 4) HR literature for PFS and/or OS were not provided.We used a variety of search tools to conduct a comprehensive search of the literature. 1) Retrieval of the computer literature database: ① key words include immunotherapy, sintilimab, tisliezumab, pembrolizumab, camrelizumab, sq-NSCLC, programmed cell death protein 1, programmed cell death protein ligand 1, clinical trials, RCTs, etc.,② Search PubMed, Cochrane Library, EMBASE, and CNKI in the form of keyword joint free words. Wanfang database, etc., the retrieval time limit is from the establishment of the database to April 2022. 2) manual retrieval of ASCO conference-related literature as a supplement to computer retrieval.The extracted data mainly include the following: author’s name, publication year, patient sample size, treatment methods, HRs, and 95% CI of PFS and OS.The Newcastle Ottawa scale (NOS) was used to evaluate the literature quality , and the2 test was used to analyze the heterogeneity among studies, and I2 was used to evaluate the heterogeneity. When p > 0.1 and I2 < 50%, it indicates that there is no statistical heterogeneity in each RCTs, and the fixed-effect model is used; on the contrary, on the premise of excluding clinical heterogeneity, the random-effect model is used. The publication bias was analyzed by an inverted funnel diagram, the sensitivity of each included literature was analyzed, and the test bias of the included literature was discussed.We used Revman 5.4 software provided by the Cochrane Collaboration Network for meta-analysis. All HRs included in the study were brought together to provide the overall effect size. The Cochrane χThrough database retrieval, there were 11 literatures in PubMed, 320 literature in the Cochrane Library, nine literatures in EMBASE, 24 literatures in CNKI, and 56 literatures in Wanfang database, and a total of 420 relevant literatures and eight conference papers and abstracts were obtained. After excluding duplication, case reports, reviews and irrelevant contents, 130 literatures were screened in strict accordance with the abovementioned screening process, and finally seven  studies 2 = 30%, p = 0.20. There is no statistical heterogeneity among the studies. The fixed-effect model is used for analysis. The results showed that HR = 1.59 , suggesting that chemotherapy combined with immunotherapy can significantly prolong the overall survival of patients in the treatment of sq-NSCLC, as shown in 2 = 28%, p = 0.19. There was no statistical heterogeneity among studies. A fixed-effect model was used for analysis. The results showed that HR = 1.84 , suggesting that immunotherapy combined with chemotherapy can significantly prolong the progression-free survival of patients in the treatment of sq-NSCLC, as shown in The results of the OS analysis can be obtained from seven groups of data in the seven included studies. For heterogeneity analysis, I2 = 35%, p = 0.06, which is analyzed by the fixed-effect model. The results showed that HR = 1.09 , suggesting that the incidence of adverse reactions at any level of the blood system using chemotherapy combined with immunotherapy is relatively high, and there are significant differences in individual results, as shown in 2 = 67%). With factor by factor exclusion, Caicun Zhou  discrete, neutrophil discrete, and platelet discrete). The heterogeneity analysis is carried out, with Icun Zhou  and SX Rcun Zhou  were fou2 = 19%, p = 0.22, which is analyzed by the fixed-effect model. The results showed that HR = 1.09 , suggesting that the incidence of adverse reactions at any level of the gastrointestinal system caused by chemotherapy combined with immunotherapy is relatively high, but there is no significant difference in the overall results, as shown in 2 = 52%). With factor by factor exclusion, L. Paz-Ares . The heterogeneity analysis is carried out, with IPaz-Ares  and LuisPaz-Ares  are the 2 = 70%, p = 0.002. The random-effect model is used for analysis. Four studies can obtain the data of any level of adverse reactions (including AST and ALT) of liver function in patients with sqnsclc treated by chemotherapy combined with immunotherapy. The heterogeneity is analyzed, with I2 = 70%, p = 0.002. The random-effect model is used for analysis. The results showed that HR = 1.58 , suggesting that the incidence of adverse reactions at any level of liver function using chemotherapy combined with immunotherapy is relatively high, and there is a significant difference in the results, as shown in 2 = 72%) and ALT (I2 = 61%) indexes. With factor by factor exclusion, Caicun Zhou (2 of AST and ALT was 0% after exclusion.Four studies can obtain the data of any level of adverse reactions (including aspartate aminotransferase (AST) and alanine aminotransferase (ALT)) of liver function in patients with sq-NSCLC treated by chemotherapy combined with immunotherapy. The heterogeneity was analyzed, with Icun Zhou  and SX Rcun Zhou  were fou2 = 25%, p = 0.14, which was analyzed by the fixed-effect model. The results showed that HR = 1.04 , suggesting that the incidence of adverse reactions above grade III in the blood system using chemotherapy combined with immunotherapy is relatively high, but there is no significant difference in the overall results, as shown in 2 = 56%). Factor by factor elimination, Caicun Zhou  of patients with sq-NSCLC treated by chemotherapy combined with immunotherapy. The heterogeneity was analyzed with Icun Zhou , SX Ren cun Zhou  and Jie cun Zhou  are the 2 = 0%, p = 0.86, which is analyzed by the fixed-effect model. The results showed that HR = 0.77 , suggesting that the incidence of grade III and above adverse reactions in the digestive system using chemotherapy combined with immunotherapy is relatively high, but there is no significant difference in the overall results, as shown in Six studies can obtain data on gastrointestinal adverse reactions above grade III  in patients with sq-NSCLC treated by chemotherapy combined with immunotherapy. The heterogeneity is analyzed, with I2 = 0%, p = 1.00, which is analyzed by the fixed-effect model. The results showed that HR = 4.18 , suggesting that the incidence of adverse reactions at any level of liver function using immunotherapy combined with chemotherapy is relatively high, and there is a significant difference in the results, as shown in Four studies can obtain the data of adverse reactions above grade III of liver function (including AST and ALT) in patients with sq-NSCLC treated by chemotherapy combined with immunotherapy. The heterogeneity is analyzed, with Ip = 0.0032) and PFS (p = 0.0026), as shown in Publication bias assessment was performed only in OS and PFS. The funnel plot is symmetrical, indicating no significant publication bias . SensitiIn recent years, the treatment mode of lung cancer has changed greatly, and the prognosis has improved. The 5-year survival rate of Chinese lung cancer patients diagnosed from 2010 to 2014 has reached 20–30%. Worldwide, the survival rate of lung cancer patients has improved by 5–10% since 2000, while the survival rate of Chinese lung cancer patients has even improved by more than 10% . More thp < 0.01), and the median progression-free survival was 8.8 months , 4.9 months  in the placebo combination group . However, adverse events at level 3 or higher occurred in 67.2% of patients in the pembrolizumab-combined chemotherapy group and 65.8% in the placebo-combined treatment group. Shun Lu et al. (p = 0.0044). In addition, compared with chemotherapy alone, combination therapy has a higher remission rate and longer remission duration.At present, immunotherapy plays an excellent role in prolonging the overall survival and disease-free progression of NSCLC. PD-1/PD-L1 plays an important role in the immune escape of tumor cells. Its pathway exists in the normal immune system. PD-1 is inherently expressed on the surface of activated T lymphocytes, B lymphocytes, and natural killer cells. PD-L1, one of its ligands, is expressed on the surface of tumor cells and tumor-infiltrating immune cells. When PD-L1 is combined with PD-1, it can produce negative stimulation signals and inhibit the activation of T lymphocytes. As a result, T cells cannot engulf tumor cells, and tumor cells escape the monitoring of the immune system, that is, immune escape, which eventually leads to occurrence of tumors. Therefore, PD1/PD-L1 inhibitors activate T lymphocytes to achieve tumor inhibition. PD1/PD-L1 inhibitors commonly used in clinic include sintilimab, tisliezumab, pembrolizumab, camrelizumab, etc., Leena Gandhi et al.  showed tu et al.  the openp value of OS and PFS, indicating that patients receiving immunotherapy combined with chemotherapy had better OS and PFS than patients receiving ordinary chemotherapy. The results were similar to those of other clinical trials. However, in terms of safety, we have refined the adverse reactions into any level of adverse reactions and more than three levels of adverse reactions, mainly including hematotoxicity, hepatotoxicity, and gastrointestinal toxicity. The results showed that 1) in hematotoxicity, the risk ratio of any level of hematotoxicity in the immunotherapy combined with chemotherapy group was higher than that in the chemotherapy group, and the results were significantly different (p < 0.00001). In subgroup analysis, there was a significant difference between anemia (p = 0.03) and decreased platelet count (p = 0.0002), but there was no significant difference between decreased leukocyte count (p = 0.25) and decreased neutrophils (p = 0.05). There was no significant difference in the incidence of hematotoxicity above grade 3 between the two groups (p = 0.30). 2) In hepatotoxicity, the risk ratio of any level of hepatotoxicity in the chemotherapy combined with immunotherapy group was higher than that in the chemotherapy group, and the results were significantly different (p = 0.001). Subgroup analysis showed that the increase of AST was more significant in the immune-combined chemotherapy group (p = 0.003), and there was no significant difference in ALT between the two groups (p = 0.10). For the incidence of hepatotoxicity above grade III, the increase in ALT was more significant in the immune-combined chemotherapy group (p = 0.04), and there was no significant difference in AST between the two groups (p = 0.24). 3) In gastrointestinal toxicity, the risk ratio of any level of gastrointestinal toxicity in the chemotherapy combined with the immunotherapy group was higher than that in the chemotherapy group, and the results were significantly different (p = 0.02). For the incidence of gastrointestinal toxicity above grade 3, there was no significant difference between the two groups (p = 0.24), but subgroup analysis showed that the incidence of gastrointestinal toxicity above grade 3 in the combined group was significantly higher and significant (p = 0.02). The abovementioned studies suggest that in clinical treatment, chemotherapy combined with immunotherapy as a first-line drug for sq-NSCLC is more effective than chemotherapy alone and can significantly prolong OS and PFS. At the same time, the incidence of adverse reactions is higher than that of chemotherapy. In clinical diagnosis and treatment, we should pay more attention to anemia, changes in platelet count, hepatotoxicity, and some conventional gastrointestinal adverse reactions to treat symptomatic treatment as soon as possible and reduce side effects.In this study, we evaluated the efficacy and safety of chemotherapy combined with immunotherapy as the first-line treatment for advanced or metastatic squamous NSCLC, with OS and PFS as the primary outcomes and adverse reactions as the secondary outcome. The disease was limited to the more difficult to cure sq-NSCLC. The results showed that, in terms of effectiveness, chemotherapy combined with immunotherapy improved the HR and This study also has some limitations: 1) after systematic retrieval and screening, only seven literatures were included for systematic evaluation and meta-analysis, and the sample size is too small; 2) the heterogeneity of individual statistical results may affect the credibility of the research results. This study will continue to update the clinical data and timely supplement the included literature so as to provide a scientific medication basis for the clinical treatment of sq-NSCLC.


Coronary artery bypass grafting (CABG) is associated with both cardiovascular disease (CVD) and non-CVD traits. In addition, women’s prognosis after coronary events and revascularizations is worse than in men. As the course of CVD in women differs from that of men, we performed a phenome-wide analysis on the sex differences in CABG -related morbidity and mortality.We performed an untargeted analysis on the sex differences in predictors and outcomes of CABG. We studied a sample of 176,680 FinnGen participants, including 5,950 individuals who underwent CABG  and were followed between 1998 and 2019. Over 1,100 different traits were analyzed for both sexes and the results were adjusted with age, smoking status and BMI. Cox proportional hazards models with sex-trait interactions were used to estimate the associations between (1) traits and incident CABG; and (2) CABG and incident traits.p-values < 0.03). After CABG, men had 2.5-fold (p = 3.1E−15) and women 6.3-fold (p = 9.4E−08) greater risk of cardiac death compared to same-sex individuals who did not undergo CABG (p for interaction 8.2E−4). Moreover, the risk of death in women remained higher even 12 years after CABG, whereas the long-term risk of death in men was not increased, compared to same-sex individuals who did not undergo CABG.In women, CABG was more strongly related to greater increases in risk of diseases such as hypertension, Alzheimer’s, aortic aneurysms, gout, and chronic kidney disease compared to risk increases observed in men (all interaction The adverse outcomes after CABG, both quantity and quality, also appear to differ between men and women. In women, CABG is related to greater long-term increases in risk of cardiac death and several other disease states than in men. Consideration should therefore be given to whether women receive adequate long-term post-operative therapy and follow-up as CABG is not associated with equally improved cardiovascular disease prognosis in women than in men. Outcomes of coronary heart disease (CHD) and coronary artery bypass crafting (CABG) have constantly improved over the past decades –3. HowevThe obvious benefits of CABG are significantly improved quality of life  and decrSome sex differences in the CABG-related morbidity are known to exist . To our The original study sample comprised 309,154 individuals from the FinnGen Data Freeze 7 which included Finnish participants from national hospital biobanks, prospective epidemiologic studies, and disease-based cohorts. All participants in the FinnGen study were linked to data from the nationwide National Hospital Discharge, Cause of Death, Cancer, and Medication Reimbursement registers by using personal identification codes.From the original study sample 132,474 individuals with missing BMI and smoking data were excluded from the study and thus the final study sample consisted of 176,680 individuals . Of these participants, 5,950  individuals underwent CABG surgery during follow-up. All study participants provided an informed written consent before their participation in the FinnGen study. This study protocol was approved by The Coordinating Ethical Committee of the Hospital District of Helsinki and Uusimaa, as described in the n ≥ 1 were defined in the FinnGen study and thus formed 8,364 pairs of events (traits preceding CABG and CABG preceding traits). However, event pairs with any subgroup size of less than 10 individuals were excluded from the study to comply with the privacy protocol of the FinnGen study.The analysis period extended from January 1, 1998, to December 31, 2019. The predictor- and outcome-traits were defined by ICD and ATC codes as described in the p-values for multiple testing and false discovery rate (FDR) using Benjamini–Hochberg correction . BMI was defined as weight (kg)/height2 (m2). Height and weight for BMI calculation were measured by nurses and smoking status was determined by self-report. We excluded from the analyses individuals that had outcome before baseline or had outcome event before the predictor event. We ignored the time for predictor events before the start of the study (if the predictor preceded the study start).We used a case-cohort design with Cox’s regression models to study the associations between different traits and CABG in men and in women separately . The cohrrection . Event pn > 200 and associations outside these thresholds limits were only reported in the p-value as the rule-in criteria.From sex-stratified Cox regression models a ratio of hazard ratios (rHR = HR women/HR men) was calculated for each predictor-CABG pairs and CABG-outcome pairs. For the main results reported in To further investigate the impact of sex on all-cause of mortality, we used a case-control approach for men and women who underwent CABG. Each case  was then matched with a control  1:1 for sex, birth year, BMI and smoking status using nearest neighbor matching with propensity score as distance. This resulted 999 cases and 999 controls for females and 5,222 cases and 5,222 controls for males. For cases, age of the first CABG was used as index age, while for controls index age was either age at year 1998 or 0, if birth year was after year 1998. An individual was included only if the end of follow up was after the index age. Using this dataset, we created Kaplan Meier curves  and logiPython 3.7.3.  and R 4.0.4  was used for the statistical analyses.2. Mean BMI was 27.3 ± 5.8 kg/m2 in women, and 27.5 ± 4.6 kg/m in men, respectively. In total, 27,196 (32.8%) of men and 17,454 (19.1%) of women were past or current smokers. A total of 5,950  individuals underwent CABG surgery during follow-up period. The mean age at the time of the procedure was 64.7 ± 9.4 years and median follow-up time for CABG was 9.3 ± 6.1 years. From all potential 8,364 event pairs, we performed 1,192 analyses where trait preceded CABG procedure and 1,423 analyses where CABG procedure preceded trait  from FinnGen database Data Freeze 7. The mean birth year of the sample was 1957.6 ± 18.4, 52.5% were women, 25.6% were active smokers, and the mean BMI was 27.4 ± 5.3 kg/med trait . The ICDWe observed five traits that were more strongly associated with future CABG in women than in men . These tWe observed several sex differences in the associations between CABG and incident traits . In geneThe risk of cardiac death after CABG was 2.5- and 6.3-fold in men and in women as compared with controls of the same sex, respectively . In the In the logistic regression analysis, an increased odds for all-cause death were observed in women with CABG compared to matched women during follow-up . The oddIn a systematic analysis of 5,950 CABG patients and over 1,100 different traits from the FinnGen study, we evaluated the sex-dependent differences in mortality and morbidity before and after CABG. In women, CABG was more strongly related to greater increases in risk of diseases such as hypertension, Alzheimer’s, aortic aneurysms, gout, and chronic kidney disease compared to risk increases observed in men. We observed that the risk for cardiac death was significantly higher in women after CABG compared to women who did not undergo CABG. The risk of cardiac death for men who underwent CABG was also higher than in the matched control population without CABG. However, the combined acute and long-term risk of death was 2.5-fold greater in women than in men, when compared to non-operated counterparts of the same sex.Female sex has previously been associated with increased cardiovascular mortality and morbidity after CABG , 5, 23. Sex differences in mortality after CABG are thought to be explained by a greater comorbidity burden in women. Both type 1 and 2 diabetes are associated with excess CVD risk in women as compared to men . In thisThoracic aortic aneurysms were observed to associate strongly with CABG in women both before and after CABG . Male seThe similar results observed for COPD further emphasize the importance of smoking as a strong CABG risk factor in women . In thisDisorders of the choroid and retina were more strongly related to risk of CABG in women than in men. This relation was similar when CABG was the predictor variable. This miscellaneous category included phenotypes, such as macular degeneration, retinal breaks, diabetic retinopathy, maculopathy, and preretinal fibrosis, which all share risk factors with CVD. Smoking and age are risk factors for macular degeneration in both women and men, but obesity, hypertension, and low physical activity are associated with increased risk of macular degeneration only in women . DiabetiWe also observed that diseases of ear and mastoid process were associated with future CABG particularly in women. This phenotype consists of several miscellaneous diseases such as sensorineural hearing loss, benign paroxysmal vertigo, and infections of the outer and middle ear. Hearing loss is known to associate with atherosclerosis and cardiovascular disease with a possible underlying mechanism of microvascular impairment , 32. ThiCABG was also associated with several other incident traits after the operation. For instance, CABG was a stronger risk factor for Alzheimer’s disease in women than in men. This is could be explained by the common risk factor profiles associated with CVD and Alzheimer’s, including hypertension, diabetes and obesity . AnotherCoronary artery bypass grafting was found to associate with incident gout more strongly in women than in men . HyperurCoronary artery bypass grafting in women was associated with incident CKD and the risk was 2.0-fold compared to men. Acute kidney injury is common immediately after CABG and also a risk factor for progression to later CKD and increases morbidity and CVD mortality . This seHistory of CABG in women was more strongly associated with incident metabolic disorders, comprised of hypercholesterolemia, hyponatremia or hypokalemia, in women than in men. As expected, CABG was associated with increased risk of hypercholesterolemia after CABG. However, this risk was greater in women than in men. This association most likely reflects that women are more susceptible of post-operative electrolyte disorders and less aggressive lipid-lowering therapy.Further, CABG was related to a reduced risk of knee replacement surgery in women compared with men. The research on the relation of osteoarthrosis and CVD has provided conflicting results. Some publications have suggested that individuals with osteoarthrosis, and particularly women, have an increased CVD risk –37, wherFinally, the phenotype other heart diseases was observed to associate more robustly with future CABG in women than in men. This phenotype includes various disease entities such as heart and valve infections, cardiomyopathies, conduction disorders, arrythmias, and various types of heart failure. This heterogeneity of the diagnoses renders further interpretation of the observed association challenging. Similarly, the phenotype malaise and fatigue, with an HR of 9.4 in women after CABG, is a very unspecific diagnosis, but this finding may represent poorer control of symptoms in women.A limitation and strength of this untargeted epidemiological study is the large number of different traits available for statistical analysis. Despite adjustments for age, BMI and smoking status, the unavailability of exact measurements of lipid-, glucose- and blood pressure levels prevents a more detailed assessment of individual risk profiles. Also, detailed information on ejection fraction, number of coronary bypasses, and the acuity of surgery were not available in this register-based study. Furthermore, as in other studies, the number of women who underwent CABG was low compared to that of men. To address these potential biases, we adjusted for multiple testing and used clinically significant thresholds of HR > 1.5 or HR < 0.66 for the main results. Moreover, as statistical associations do not indicate causality, more research is needed in order to discover the mechanisms of the observed connections to improve the outcomes in women with CHD.Women are at higher risk for diseases after CABG, which further increase the risk of death. After CABG, the relative risk of long-term death in women is significantly higher than in men. Consideration should be given to whether women receive adequate treatment post-CABG, as CABG does not improve life expectancy in women as much as in men.www.fingenious.fi and at www.FinnGen.fi/en.The FinnGen data may be accessed through Finnish Biobank’s Finngenious portal at Patients and control subjects in FinnGen provided informed consent for biobank research, based on the Finnish Biobank Act. Alternatively, separate research cohorts, collected prior the Finnish Biobank Act came into effect (in September 2013) and start of FinnGen (August 2017), were collected based on study-specific consents and later transferred to the Finnish biobanks after approval by Fimea (Finnish Medicines Agency), the National Supervisory Authority for Welfare and Health. Recruitment protocols followed the biobank protocols approved by Fimea. The Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS) statement number for the FinnGen study is Nr HUS/990/2017. The FinnGen study was approved by Finnish Institute for Health and Welfare , Digital and population data service agency , The Social Insurance Institution , Findata , Statistics Finland [permit numbers: TK-53-1041-17 and TK/143/07.03.00/2020 (earlier TK-53-90-20)]. The Biobank Access Decisions for FinnGen samples and data utilized in FinnGen Data Freeze 7 include: THL Biobank BB2017_55, BB2017_111, BB2018_19, BB_2018_34, BB_2018_67, BB2018_71, BB2019_7, BB2019_8, BB2019_26, BB2020_1, Finnish Red Cross Blood Service Biobank 7.12.2017, Helsinki Biobank HUS/359/2017, Auria Biobank AB17-5154 and amendment #1 (August 17 2020), Biobank Borealis of Northern Finland_2017_1013, Biobank of Eastern Finland 1186/2018 and amendment 22 §/2020, Finnish Clinical Biobank Tampere MH0004 and amendments (21.02.2020 & 06.10.2020), Central Finland Biobank 1-2017, and Terveystalo Biobank STB 2018001. The patients/participants provided their written informed consent to participate in this study.JN wrote the original draft and revisions. AK and JP analyzed and interpreted the patient data. JA and TN planned, reviewed, and edited the manuscript. All authors contributed to the article and approved the submitted version.


To use interrupted time-series analyses to investigate the impact of the coronavirus disease 2019 (COVID-19) pandemic on healthcare-associated infections (HAIs). We hypothesized that the pandemic would be associated with higher rates of HAIs after adjustment for confounders.We conducted a cross-sectional study of HAIs in 3 hospitals in Missouri from January 1, 2017, through August 31, 2020, using interrupted time-series analysis with 2 counterfactual scenarios.The study was conducted at 1 large quaternary-care referral hospital and 2 community hospitals.All adults ≥18 years of age hospitalized at a study hospital for ≥48 hours were included in the study.In total, 254,792 admissions for ≥48 hours occurred during the study period. The average age of these patients was 57.6 (±19.0) years, and 141,107 (55.6%) were female. At hospital 1, 78 CLABSIs, 33 CAUTIs, and 88 VAEs were documented during the pandemic period. Hospital 2 had 13 CLABSIs, 6 CAUTIs, and 17 VAEs. Hospital 3 recorded 11 CLABSIs, 8 CAUTIs, and 11 VAEs. Point estimates for hypothetical excess HAIs suggested an increase in all infection types across facilities, except for CLABSIs and CAUTIs at hospital 1 under the “no pandemic” scenario.The COVID-19 era was associated with increases in CLABSIs, CAUTIs, and VAEs at 3 hospitals in Missouri, with variations in significance by hospital and infection type. Continued vigilance in maintaining optimal infection prevention practices to minimize HAIs is warranted. National Healthcare Safety Network (NHSN) data have demonstrated higher rates of central-line–associated bloodstream infection (CLABSI), catheter-associated urinary tract infection (CAUTI), and ventilator-associated events (VAEs), compared to the prepandemic period and as a function of COVID-19 case load. However, these studies have lacked correlation between patient-level risk factors and development of infections during hospitalization.During the coronavirus disease 2019 (COVID-19) pandemic, secondary bacterial infections have been seen in patients with COVID-19. However, consequences of natural phenomena can also be studied given a well-defined change point. We used interrupted time-series analysis to investigate the impact of the COVID-19 pandemic on healthcare-associated infections (HAIs) at 3 hospitals in St. Louis, Missouri. Time series of HAIs were compared before and after March 2020, when COVID-19 cases were first detected regionally. Our goal was to determine whether the pandemic was associated with higher HAI rates after adjusting for confounding variables. Such an increase has implications for infection prevention, antimicrobial stewardship, and other processes of care.Interrupted time-series analyses are used in public health research to estimate changes in a measured outcome before and after an intervention.This retrospective observational study was approved by the Washington University School of Medicine Institutional Review Board with a waiver of informed consent. Patient-level data were obtained for hospitalizations during January 1, 2017, through August 31, 2020, from the BJC HealthCare Clinical Data Repository. Data were obtained from 3 hospitals in the BJC Healthcare System that treated most COVID-19 patients, including 1 large quaternary-care referral hospital and 2 community hospitals. Infection control surveillance data reported to NHSN were collected from each of the 3 hospitals. CLABSIs, CAUTIs, and VAEs were defined according to CDC definitions, adjudicated by each hospital’s team of infection preventionists. Counts of CLABSIs, CAUTIs, and VAEs, as well as the number of central-line, urinary catheter, and ventilator days were aggregated monthly for each facility. The prepandemic period was defined as January 1, 2017–February 29, 2020. The pandemic period was defined as March 1, 2020–August 31, 2020. Medical record review was performed for admissions with a COVID-19 ICD-10-CM diagnosis code without a positive laboratory result to confirm a clinical diagnosis of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection.International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes were used to compute the Elixhauser comorbidity index for each admission, and these data were averaged by facility and month. The number of beds at each facility was treated as a constant throughout the study period.Inpatient stays at each facility of >48 hours duration were used to define monthly admission counts, total days admitted, and average length of stay. In addition,  Other covariates included monthly admission counts, monthly mean length of stay, monthly mean in-hospital Elixhauser comorbidity index, and number of beds, as described above. Harmonic terms were used to account for secular trends unrelated to the pandemic.Monthly CLABSI, CAUTI, and VAE counts were modeled separately using generalized linear models with a Poisson distribution and log link. Data from all facilities across all months were pooled into a single model for each type of HAI, and the monthly days of exposure  were included as covariates to account for differences among hospitals. Mixed-effects models with hospital as a random effect were performed, but the estimated variance component was not significantly different from zero for any HAI. The effects of COVID-19 were modeled using an indicator variable taking the value zero prior to March 2020 and one thereafter to allow for change in the level of each HAI, and as a linear term expressed as the rate of COVID-19 cases per 10,000 inpatient days to allow for change in the trend. Quasi-Poisson and negative binomial models were fitted to check for improvement in the mean–variance relationship.Model fit was assessed graphically by examining time series of residuals stratified by facility, plots of residuals and linear predictors, and plots of the estimated mean–variance relationship implied by each model. An estimate of the dispersion parameter was also computed.The excess number of each HAI attributable to the COVID-19 pandemic  was estimated using 2 counterfactual scenarios. In the first , the values of all monthly covariates were left unchanged except the rate of COVID-19 cases per 10,000 inpatient days, which was set to zero across all pandemic period months. Predicted numbers of each HAI during the pandemic period were then generated from the original fitted model and compared to the actual number of infections. The intention of this scenario was to allow for pandemic-related disruptions to hospital operations while exploring how the number of infections would have differed had no COVID-19 cases been seen by any facility. Forecasts were computed using seasonal ARIMA models, with the parameters of each model chosen using the procedure described by Hyndman and Khandakar and implemented by Hyndman and Athanasopoulos. Finally, the facility-specific forecasts were incorporated into the alternative models, generating predicted values and 95% confidence intervals for each HAI during the pandemic period, which were compared to the actual number of infections.Scenario 2 envisioned a situation in which the pandemic never happened . First, alternative models were fit using prepandemic data with all covariates from the original models retained except those designed to capture the effects of COVID-19, that is, all covariates were retained except for the pandemic indicator variable and the rate of COVID-19 cases per 10,000 inpatient days. Then, hypothetical values were generated for each pandemic period monthly covariate by forecasting based on the prepandemic time-series associated with each facility.As a secondary analysis, total attributable HAIs under both counterfactual scenarios were re-estimated after excluding the first 2 months of the pandemic period (March and April 2020) in an attempt to isolate the effect of COVID-19 cases from the general disruption in hospital operations that occurred at the start of the pandemic .Data management was performed using SAS version 9.4 software , and analyses were conducted using R version 4.1.2 software .A comparison of facility characteristics during the prepandemic and pandemic periods is provided in Table 1 and 2, with the predicted values adjusted for covariates. Infection control specialists at hospital 1 documented 78 CLABSIs during the pandemic period as well as 33 CAUTIs, and 88 VAEs. During the same period, hospital 2 documented 13 CLABSIs, 6 CAUTIs, and 17 VAEs. Hospital 3 documented 11 CLABSIs, 8 CAUTIs, and 11 VAEs.The time series of the actual number of HAIs as well as the predicted number of HAIs under each counterfactual scenario are displayed in Figure 3 and 4, adjusted for monthly admission counts, mean length of stay, mean Elixhauser comorbidity index, number of hospital beds, time trends unrelated to the pandemic, and central-line, urinary catheter, and ventilator days for the corresponding infection. Confidence intervals for hospital 1 included zero under all scenarios, excepting CLABSIs under the “no cases” scenario. At hospital 2, excess CLABSI estimates excluded zero under both scenarios: no cases, 9.5  and no pandemic, 7.6 . Excess CAUTIs at hospital 2 were also significantly different from zero under all scenarios: no cases, 4.2  and no pandemic, 4.6 . Also, at hospital 2, VAEs were significant under the “no pandemic” scenario: 8.2 . At hospital 3, CLABSIs were significantly higher in both scenarios: no cases, 7.3  and no pandemic, 5.3 . Excess CAUTIs were not significantly different from zero under either scenario, and excess VAEs excluded zero under the “no pandemic” scenario at 3.8 .Total excess HAIs at each facility attributable to the pandemic are shown in Figure 1 and 2), hospital 1 had 63 CLABSIs, 25 CAUTIs, and 59 VAEs. Hospital 2 had 4 CLABSIs, 3 CAUTIs, and 11 VAEs, and hospital 3 had 7 CLABSIs, 7 CAUTIs, and 7 VAEs. Estimates of excess HAIs were generally similar to those in the primary analysis . Hospital 1 had increased case numbers of all infection types across scenarios, but these were not significant. At hospital 2, CLABSI estimates increased but not significantly. At hospital 2, excess CAUTIs remained significantly greater than zero under all scenarios: no cases, 2.3  and no pandemic, 2.3 . At hospital 2,VAEs were still significant under the “no pandemic” scenario at 5.2 . At hospital 3, CLABSIs were no longer significantly different from zero under “no cases” but remained significantly higher under “no pandemic” at 3.2 . In contrast to the primary analysis, CAUTIs were significantly higher under both scenarios at hospital 3: no cases, 4.5 ; no pandemic, 2.6 . The estimate of VAEs under the “no pandemic” scenario was no longer significantly different from zero (Supplementary Table 1).After excluding March and April 2020 from the pandemic period compared to the counterfactual scenarios, were higher during the pandemic period, consistent with prior literature. Other contributing factors may have included increased length of hospital stay, an overburdened healthcare system, high patient-to-staff ratios due to increased staffing issues, and staff burnout.Reasons for the higher rates of HAIs during the pandemic period  may be multifactorial, including personal protective equipment shortages and changes in infection prevention practices  as has been suggested previously.Our study was limited by its regional nature, with all 3 hospitals located in the St. Louis metropolitan area, which may not be representative of patient populations and risk factors for HAIs across the state and country. In addition, because these are observational data, we cannot be certain that the results are a direct result of the pandemic and not some other confounding factor. To best address this, we included a dichotomous term for the pandemic and a separate term for rates of COVID-19 cases. Our study was limited by the amount of data available, which reduced our statistical power; thus, some of the null results we found are likely due to lack of power rather than lack of true effect. This factor can be explored further in future studies with larger cohorts.Our study was strengthened by the rigorous analytical method utilized. We investigated counterfactual scenarios to estimate the effects of different aspects of the pandemic on the number of HAIs: one  was intended to separate the effect of COVID-19 caseload from that of other pandemic-related disruptions, and a second  was designed to assess the impact of the pandemic overall by generating predicted HAIs in a scenario without the pandemic. Importantly, these hypothetical HAIs under ‘no pandemic’ were based on forecasts of pre-existing trends in related covariates, rather than an assumption of a simple linear trend. To our knowledge, this is the first time this approach has been used to understand HAI rates during the COVID-19 pandemic.In conclusion, the early COVID-19 era was variably associated with increases in CLABSIs, CAUTIs, and VAEs at 3 hospitals in a metropolitan region in Missouri, with variations in risk by hospital. Continued vigilance in maintaining optimal infection prevention practices to minimize HAIs is warranted, including hand hygiene, minimizing unnecessary device use, and promoting antimicrobial stewardship.


Influencing factors with a high contribution to humidity were extracted using LightGBM to reduce the complexity of the model, and required hyperparameters in SVR were optimized, adopting the CGWO algorithm to avoid the local extremum problem. The experimental results indicated that the proposed LightGBM–CGWO–SVR model outperformed eight existing models used for comparison on all evaluation metrics; it achieved minimum values of 0.0662, 0.2284, 0.0521, and 0.0083, in terms of MAE, RMSE, MSE, and NRMSE, respectively, and a maximum value of 0.9973, in terms of the R2 index.Accurately predicting humidity changes in sheep barns is important to ensure the healthy growth of the animals and to improve the economic returns of sheep farming. In this study, to address the limitations of conventional methods in establishing accurate mathematical models of dynamic changes in humidity in sheep barns, we propose a method to predict humidity in sheep barns based on a machine learning model combining a light gradient boosting machine with gray wolf optimization and support-vector regression (LightGBM–CGWO–SVR). Influencing factors with a high contribution to humidity were extracted using LightGBM to reduce the complexity of the model. To avoid the local extremum problem, the CGWO algorithm was used to optimize the required hyperparameters in SVR and determine the optimal hyperparameter combination. The combined algorithm was applied to predict the humidity of an intensive sheep-breeding facility in Manas, Xinjiang, China, in real time for the next 10 min. The experimental results indicated that the proposed LightGBM–CGWO–SVR model outperformed eight existing models used for comparison on all evaluation metrics. It achieved minimum values of 0.0662, 0.2284, 0.0521, and 0.0083 in terms of mean absolute error, root mean square error, mean squared error, and normalized root mean square error, respectively, and a maximum value of 0.9973 in terms of the R Sheep farming for meat is a leading industry in the livestock economy of rural inland Northwest China , and large-scale intensive farming is the primary mode of production . However2 concentration, as input variables. Zou et al. [2; however, the prediction accuracy for humidity was very low for all models. Although these studies on the prediction of greenhouse humidity, based on neural network, provide some convenience for production management, they also have the disadvantage of low prediction accuracy. Besides unsatisfactory prediction accuracy, the above single prediction methods, based on data drive, face other issues, such as poor robustness, the complexity of constructing the models, and difficult selection of neural network parameters in the actual prediction process. In particular, environmental data collected by sensors from large-scale sheep breeding bases are characterized by strong coupling, high dimensionality, nonlinearity, and a large time lag. As a single prediction model considers all the parameters, this approach leads to a complex model structure and poor generalization performance, and, thus, limits the final prediction accuracy. Therefore, the use of a hybrid model to predict such strongly coupled, high-dimensional, and nonlinear parameters has attracted considerable attention and has neem a topic of active research in recent years.As a typical machine learning method, the linear method was discussed earlier in the prediction of pig house environment and humidity. Banhazi et al. ,7 used au et al.  used a cu et al.  used thr2 prediction model based on a wavelet neural network designed to consider indoor and outdoor temperatures, indoor humidity, and ventilation rates as input variables. He and Ma [A typical hybrid design helps remove noise, reduce dimensionality, or extract features from high-dimensional input data of models, e.g., wavelet transform and principal component analysis, and, subsequently, uses the obtained data as an input to construct a predictive model using machine learning or deep learning methods. Some hybrid models have been applied to predict humidity and other environmental parameters in livestock houses or greenhouses. For example, Besteiro et al.  developee and Ma  extractee and Ma  as a vare and Ma ,18,19. Se and Ma , an itere and Ma ,21,22.In data-driven predictive modeling algorithms, the support-vector regression (SVR) approach rests on a stronger mathematical theoretical foundation than neural-network-based “black box” predictive models . It can As the SVR parameter selection is often stochastic and empirical, inappropriate parameter combinations affect the performance and prediction accuracy of such models. Thus, for SVR prediction models, the best parameter combination should be selected to obtain good prediction results. The gray wolf optimization (GWO) algorithm proposed by Mirjalili et al.  possesseTherefore, to improve the lower performance of the existing humidity prediction methods for livestock houses or greenhouses, and overcome the shortcomings of a single SVR model, which may be difficult to adapt to sheep barn humidity prediction, we propose a hybrid prediction model for ambient air humidity in sheep barns based on the proposed combined LightGBM–CGWO–SVR method, to predict the air humidity after 10 min. First, redundant or overlapping information on the environmental features obtained from the sensors is filtered out using the LightGBM model’s built-in dimensionality reduction technique, to avoid excessive computational complexity and overfitting caused by the direct input of multidimensional features. Subsequently, the SVR model hyperparameters are searched and optimized using the GWO algorithm, improved by the chaos operator for better accuracy and reliability.2, was selected to collect the experimental data. The facility mainly breeds and raises Suffolk sheep, and the sheep barn structure is semi-enclosed and generally divided into three parts, as shown in 2.5), particulate matter 10 (PM10), total suspended particulate (TSP), CO2, ammonia, and hydrogen sulfide sensors were deployed in the main area. The ammonia, hydrogen sulfide, noise, and other sensors were installed at 3.2, 3.7, 3.1, and 3.3 m, respectively, from the ground.The source of the data was a sheep breeding facility for meat production located at 44°27′18″ N latitude and 86°10′47″ E longitude in Manas County, Changji Hui Autonomous Prefecture, Xinjiang Uygur Autonomous Region, China. The main space of the facility, with an area of ~422 mThe sensors installed in the sheep barn performed online data collection at 10-min intervals, and the data were uploaded to a remote IoT cloud service platform through a gateway. The 2000 units of data collected online from 8 to 22 February 2021, were used as the data source for this study; 70% of these data were selected for the training set, and 30% were used for the testing set to train and validate the performance of the proposed humidity prediction model. The raw environmental data are provided in As the parameters in the sheep barn environment had different magnitudes and units, which differed significantly and affected the data analysis and learning results, the influence of magnitudes between parameter sets needed to be eliminated through normalization to enable the algorithms to learn and analyze the hidden structural relations in the data more easily . In addiStep 1:Sort the sample points in descending order according to the absolute values of their gradients.Step 2:Select the top Step 3:Randomly select Step 4:Combine the large and small gradient samples Population structureIn the GWO algorithm, each GW represents a potential solution, where the optimal solution is denoted as (2)Surrounding the preyThe GW encircle the prey in the GWO algorithm as described by a mathematical model defined in Equations (4) and (5):(3)Hunting processThe (1)Improvements of update mechanism based on random coefficient vectors For GWO, exploration and exploitation capabilities are crucial to optimal performance and mainly depend on the value of the stochastic exploration coefficient Inspired by chaos theory, we introduced time-varying acceleration constants in (2)Algorithm implementation of CGWOIn summary, the implementation process of the proposed CGWO algorithm is given as follows:Step 1:Initialize the algorithm parameters of the GW population size Step 2:Population initialization. Randomly generate Step 3:Set Step 4:If Step 5:Update the distances and directions of the movement of individual GWs in the current pack from the Step 6:Update the random exploration vectors Step 7:Calculate the fitness of each individual GW for the current iteration Step 8:Update Step 9:For a given sample training set To control the accuracy of the fit, an insensitive loss function Penalty coefficients Using the Lagrange multipliers By solving the pairwise problem, the SVR regression function could be expressed as:In this experiment, the radial basis kernel function RBF, which is currently widely used, was considered, and expressed as:Step 1:Perform data repair on the collected data.Step 2:Use LightGBM to calculate the IG for the sheep barn environmental data after the data have been restored, and each parameter factor has been ranked in terms of feature importance, selecting strongly correlated features.Step 3:Normalize the data after dimensionality reduction, based on LightGBM, and divide the training and test sets in a ratio of 7:3.Step 4:Based on the training set, optimize Step 5:Apply the best hyperparameters obtained using CGWO, that is, A hybrid prediction model. based on the proposed LightGBM-CGWO-SVR method, was implemented for sheep barn humidity . The speThe experimental calculations were performed using a computer with an 11th Gen Intel(R) Core (TM) i7-1165G7 processor at 2.80 GHz and 16 GB of memory, running the Windows 10 64-bit operating system with the Anaconda3 platform and the Python 3.6 programming langue (64-bit). The simulation environment was completed by installing the NumPy, Sklearn, and LightGBM packages.The CGWO parameters were set as follows. The size of the wolf pack was set to 20, the random exploration vectors 2), mean absolute error (MAE), root mean square error (RMSE), normalized root mean square error (NRMSE), and mean squared error (MSE) were used as evaluation metrics to computationally assess the prediction results.In this study, the coefficient of determination (R(1)2 shown in Equation (1) was used to assess the fit between the predicted and actual humidity, and considered a value in the range of , a value closer to 1 represented a better fit between the predicted and actual humidity values.R(2)MAE was the average distance between the individual and predicted humidity data; a smaller MAE implied that the predicted value was closer to the actual humidity data (Equation (23)).(3)MSE represented the mean of the square of the difference between individual humidity data and the predicted humidity data. RMSE was the root mean square result of the MSE, which could be intuitive, in terms of the order of magnitude performance; normalized RMSE converted the RMSE value within . The smaller the MSE, RMSE, and NRMSE, the smaller the error between the predicted and true values, and the more accurate the model. They were expressed as:In the equations above, 2 achieved the highest value of 0.9973. As shown in To demonstrate the superior performance and effectiveness of the proposed LightGBM–CGWO–SVR model in predicting the humidity of sheep barn facilities, our approach was compared with the following models: GRU, SVR, extreme gradient boosting (XGBoost), GA-SVR, GWO-SVR, CGWO-XGBoost, CGWO-SVR, and LightGBM-SVR. These were computed using the same training and validation datasets, and parameters of different SVR models are shown in 2 of SVR improved by 9% and 4%, respectively. This indicated that the SVR model exhibited good adaptability to sheep barn humidity data. The real observed values of the local humidity and the predicted values of the GRU, XGBoost, and SVR models are shown in For a single model, the prediction accuracy of the SVR model was higher than that of the GRU and XGBoost models. The MAE, RMSE, MSE, and NRMSE values of the SVR model were reduced by 76%, 30%, 51%, and 30%, respectively, compared with those of GRU, and 76%, 19%, 34%, and 19%, respectively, compared with those of XGBoost. Compared with GRU and XGBoost, the R2 was improved by 1%. Compared with the GWO-optimized SVR algorithm, the MAE, RMSE, MSE, and NRMSE values were reduced by 4%, 30%, 48%, and 28%, respectively, and R2 was improved by 0.4%. Thus, CGWO outperformed GA and GWO in terms of optimization and could maintain a good balance between local and global searches. Moreover, the real measurement values of humidity localized in the validation set were compared with the predicted values obtained from the GA–SVR, GWO–SVR, and CGWO–SVR models , the SVR algorithm optimized using CGWO exhibited lower MAE, RMSE, MSE, and NRMSE values of 80%, 43%, 73%, and 48%, respectively, compared to the SVR algorithm optimized using GA, whereas RR models . Using tThe data in 2 reached 0.9940, which was only 0.39% less than that of the dataset with a lag of 10 min.In addition, autocorrelations of the observed time series data are often an important factor affecting prediction performance. 2 of 09768 and RMSE of 1.6745. However, the test set results with R2 of 0.8842 showed that the model generalization ability was still insufficient. The RNN–LSTM prediction model developed by Hongkang et al. [2 was only 0.82 for the dataset with a 10 min lag.In a previous related study, He and Ma  obtainedg et al.  achievedThe proposed LightGBM–CGWO–SVR prediction model exhibited high prediction accuracy for the collected time-series data. It could help accurately predict changes in humidity pattern in sheep barn facilities at intervals of 10 min or even 90 min. Furthermore, it could effectively adapt to nonlinear and high-dimensional data on humidity in sheep barns and exhibited superior generalization ability and prediction accuracy, compared to advanced algorithms, such as GRU and XGBoost models. It is possible to apply constructed combination models to predict and regulate air humidity in intensive sheep farming barns.It should be noted that the data was acquired in February, when Xinjiang is in winter and very cold. For this reason, the sheep house was not always strongly ventilated, but ventilated intermittently and regularly. That is to say, a large part of the humidity data in this data set was not significantly affected by ventilation. Of course, the ventilation factor should not be ignored in model construction for the warm season, when the sheep house would be frequently ventilated.(1)2 concentration, light intensity, air temperature, and PM2.5 were finally screened from the nine variables, including air humidity, CO2 concentration, PM2.5, PM10, light intensity, noise, TSP, NH3 concentration, and H2S concentration, and used for modeling, which effectively improved the computational efficiency of the model and its accuracy.The characteristics of the environmental factors of the sheep barn were screened using LightGBM without considering environmental variables with small effects on humidity. The key influencing factors of CO(2)The CGWO algorithm obtained by introducing chaotic operators in this study retained the advantages of a simple structure, few control parameters, and straightforward implementation of the GWO algorithm, while improving the global search capability of the traditional GWO algorithm and solving the problem of randomness and empiricality in the selection of SVR parameters. The optimized SVR algorithm exhibited higher prediction accuracy and better generalization ability than an SVR algorithm optimized using GA and GWO.(3)2 achieved the highest value of 0.9973. The results indicated that the proposed model can provide an effective guide for the accurate prediction of humidity in sheep barns in for Suffolk sheep, in Xinjiang, and could provide a reference for future research on the prediction of other environmental factors, such as NH3 and H2S concentration.The performance of the proposed LightGBM–CGWO–SVR model in predicting the humidity for 10 min in a sheep barn facility exceeded that of the GRU, SVR, XGBoost, GA–SVR, GWO–SVR, CGWO–XGBoost, CGWO-SVR, and LightGBM–SVR models. The minimum values of the MAE, RMSE, MSE, and NRMSE indices were 0.0662, 0.2284, 0.0521, and 0.0083, respectively, and RThe final simulation results of the proposed LightGBM–CGWO–SVR model and a comparison with other advanced models implied the following points:


To discuss the evaluation value of CT in the efficacy of neoadjuvant chemotherapy in patients with ovarian cancer.  The clinical, pathological, and CT imaging information of 72 patients with ovarian cancer treated in our hospital from January 2018 to January 2022 were retrospectively analyzed. CT examination and pathological examination were compared to evaluate the efficacy of neoadjuvant chemotherapy. P > 0.05. The CT manifestations of lymphadenectasis, degree of peritoneal thickening, ascites, and maximum length diameter of the mass were compared between the patients before and after chemotherapy, P < 0.05. According to RECIST 1.1, there were 1 (1.39%) CR, 38 (52.78%) PR, 29 (40.28%) SD, and 4 (5.56%) PD. The comparison was done between RECIST 1.1 and CRS grading system, P > 0.05.  Using the CRS grading system, 26 cases (36.11%) scored 1, 42 cases (58.33%) scored 2, and 4 cases (5.56%) scored 3. CRS grading system scores of 1, 2, 3, and 4–7 patients were compared,  CT could be used to evaluate the efficacy of neoadjuvant chemotherapy for ovarian cancer. As diseases of the female reproductive system, ovarian cancer is a common malignant tumor that occurs in the ovarian region. In China, its incidence rate ranks the third among gynecological malignant tumors, and its death rate tops the list , 2. EpitOvaries being located deep in the pelvic cavity caused early symptoms of epithelial ovarian cancer being difficult to detect. At present, there is still a lack of an effective method for early detection of microscopic pathological changes in ovarian histology. According to the data released by the International Society of Obstetricians and Gynecologists, more than 70% of epithelial ovarian cancers are already at stages III-IV when diagnosed, and the patients have developed abdominal and pelvic metastasis or even distant metastasis , 6. At pThe grading system of the chemotherapy response score (CRS) is the golden criterion for appraising the efficacy of neoadjuvant chemotherapy in ovarian cancer, but its clinical application is limited because of its traumatic nature , 10. TheThe clinical, pathological, and CT imaging information of ovarian cancer patients admitted to our hospital from January 2018 to January 2022 were retrospectively analyzed. Inclusion criteria were as follows: ovarian cancer , 14 was The flow diagram of the method is shown in μm). Sections were stained with H&E and read by a pathologist.After cytoreductive surgery, tissue blocks from the lesion area of epithelial ovarian cancer were taken and fixed in 10% neutral formaldehyde solution, followed by dehydration, paraffin embedding, and sectioning  was injected intravenously. The injection dose was 1.5 mL/kg, the injection speed was 3 mL/s, and the arterial phase and venous phase scans were performed 30 s and 60 s after injection. Image analysis was conducted independently by two CT diagnostic physicians with titles above deputy director in the blind method. CT findings of patients before and after chemotherapy such as the nature of tumor, lymphadenectasis, invasion of the surrounding tissue, degree of peritoneal thickening, ascites, and maximum length diameter of the mass were observed. Consensus was reached through discussion if differences occurred.The nature of tumor included solid, solid-cystic, and cystic. Solid means that the solid component accounts for at least 2/3 of the tumor component, solid-cystic means that the cystic component accounts for at least 2/3 of the tumor component, and cystic means that it is between cystic and solid. Lymphadenectasis was determined when the short diameter of the lymph node (measured for 3 times and averaged) was greater than 10 mm. Surrounding tissues include the uterus, bilateral accessories, bladder, ureter, rectum, sigmoid colon, and basin wall. If the CT signs of the patient were indistinct boundaries between masses and tissues, lobulated or irregular thickening of the edges, cloudy, fuzzy or disappeared fatty layer between organs, and uneven mild to moderate enhancement of enhanced scanning, it was judged as invasion of the surrounding tissue; otherwise, it was not invasion of the surrounding tissue. The degree of peritoneal thickening includes grades 1, 2, and 3. Grade 1 was the greater omentum with stained thickening, often with many fine lines and occasionally with a few small nodules, grade 2 was the greater omentum with multiple isolated nodules, and grade 3 was the omentum with nodular thickening fusing with each other, forming “omentum cake sign.” Ascites includes small, medium, and large. The small amount of ascites was defined as a fluid density shadow at the peritoneal reflexes. Medium ascites was defined as uniform crescent-shaped fluid density shadow around the liver, spleen, or bilateral paracolic sulcus. The large amount of ascites was defined as uniform low-density shadows around the abdominal organs, which clustered toward the center under pressure, and the mesangial adipose tissue squeezed it into a circuitous shape. The PACS system was used to measure the maximum length diameter of the mass. The measurement range was the maximum layer of the mass in the axial image. A total of 3 times were measured, and the average value was taken.Evaluation was performed using Response Evaluation Criteria in Solid Tumors 1.1 (RECIST 1.1) , 16. ForFor nontarget lesions, CR meant that nontarget lesions disappeared and tumor markers returned to the normal level, and the short diameter of lymph nodes was less than 10 mm. Noncomplete remission (non-CR) or nonprogressive disease (non-PD) was the presence of one or more nontarget lesions persisting and/or higher-than-normal levels of tumor markers. PD refers to the presence of one or more new lesions or definite progression of existing nontarget lesions, and an increase in the size of nontarget lesions can lead to increased tumor load. PD referred to the presence of one or more new lesions or definite progression of existing nontarget lesions, and an increase in the size of nontarget lesions could lead to increased tumor load.After the efficacy evaluation of target and nontarget lesions, the overall efficacy was evaluated. CR: both target and nontarget lesions reached CR, and no new lesions appeared. PR: target lesions reached CR, nontarget lesions reached non-CR or non-PD, and no new lesions appeared; target lesions reached CR, nontarget lesions were not fully evaluated, and no new lesions appeared; target lesions reached PR, nontarget lesions reached non-PD or were not fully evaluated, and no new lesions appeared. SD: target lesions reached SD, nontarget lesions reached non-PD or were not fully evaluated, and no new lesions appeared. PD: if the target or nontarget lesions reached PD, there was no requirement for nontarget lesions and whether there was a new lesion; new lesions appeared, and there was no requirement for target and nontarget lesions; unsuitable for evaluation, incomplete evaluation of target lesions, nontarget lesions to non-PD, and no new lesions.The CRS grading system was used to evaluate the efficacy of neoadjuvant chemotherapy , 17. Scot-test was performed. The counting data were indicated by the number of cases or percentage, carrying out the chi-square test. When the theoretical frequency was less than 5 but greater than 0, the chi-square value should be corrected, and when theoretical frequency was less than 1, the exact probability method was used to calculate. When P was less than 0.05, the comparison was considered to be statistically significant.The data were analyzed by the SPSS 23.0 software, the measurement data conforming to normal distribution were indicated by mean ± standard deviation, and the Among the patients enrolled in this study, the number of patients aged 50–59 was the highest (40.28%), and the proportion of patients aged ≥70 was the lowest (40.28%). Forty cases (55.56%) were postmenopausal and 32 cases (44.44%) were not postmenopausal. Stage IIIC was the highest (58.33%) and stage IIA or IIB was the lowest (4.17%). Six cases (8.33%) were highly differentiated and 66 cases (91.67%) were poorly differentiated. Eleven cases (15.28%) were serous papillary adenocarcinoma, and 61 cases (84.72%) were serous cystadenocarcinoma, as given in P > 0.05, as given in The CRS grading system was used to evaluate efficacy. Twenty-six cases (36.11%) scored 1, 42 cases (58.33%) scored 2, and 4 cases (5.56%) scored 3. The comparison result was P < 0.05. The above data are given in 2The CT manifestations of lymphadenectasis, degree of peritoneal thickening, ascites, and maximum length diameter of the mass were compared between the patients before and after chemotherapy, P > 0.05 , 38 PR (52.78%), 29 SD (40.28%), and 4 PD (5.56%). The comparison between RECIST 1.1 and CRS grading system shows that P > 0.05 . CT imagNeoadjuvant chemotherapy plays a crucial part in the treatment of ovarian cancer, and its efficacy directly affects the efficacy of subsequent tumor cell reduction , 18. It The results of this research showed that the effect of the RECIST 1.1 evaluation method was indistinguishable from the CRS grading system in the evaluation of neoadjuvant chemotherapy, indicating that the effect of CT examination evaluation was basically the same as that of pathological examination evaluation, which had a certain practicality. It was consistent with relevant research results , 20. In The application of this study was to MDCT equipment for scanning the diaphragm to the pubic symphysis, with wide scanning, scanning speed, and scanning layer thickness, in addition to scan image processing software available, more levels in CT image reconstruction, and three-dimensional reconstruction of processing, so as to realize more direction adjustment to make the image clearly show the space of diseased tissue anatomy.In the process of CT examination, multiphase enhanced scanning can also be carried out to realize the dynamic observation of the relationship between the diseased tissue and blood vessels and adjacent tissues . CT can CT findings of lymphadenectasis in patients with epithelial ovarian cancer showed enlarged, cystic lymph nodes around pelvic iliac vessels, intraperitoneal cavity, and retroperitoneum. Enhanced scan showed uniform or uneven density, marked uniform enhancement, or ring enhancement. After chemotherapy, lymph nodes were significantly reduced, with short diameters generally no more than 10 mm. They were mostly round or oval in shape with smooth sharp edges, clear boundaries, solid, uniform density, and degree of enhancement, and no liquefaction necrosis of the central medulla. These were similar to the results of relevant studies , 23.Ascites usually appears in advanced epithelial ovarian cancer as a result of abdominal pelvic and peritoneal metastasis. The CT findings showed moderate to large amounts of ascites in the abdomen and pelvis. Studies had shown that tumor cells blocking lymphatic vessels is the main mechanism of ascites . For epiHowever, according to the results of this study, CT had no statistical significance in evaluating the nature of tumor and the invasion of the surrounding tissue before and after neoadjuvant chemotherapy, suggesting that those two CT findings could not provide guidance for evaluating the efficacy of neoadjuvant chemotherapy for epithelial ovarian cancer. Possible reasons are as follows: benign epithelial ovarian cancer masses were usually cystic or cystic-solid, with smooth surface and good mobility, and vesicorectal compression may occur when they become significantly enlarged. Malignant epithelial ovarian cancer tumor was solid or cystic-solid, uneven surface, fixed, and often accompanied by ascites. CT examination was difficult to distinguish the distribution of tumor properties before and after neoadjuvant chemotherapy, so it was not suitable to appraise the efficacy of neoadjuvant chemotherapy by CT examination. In the terminal of epithelial ovarian cancer, tumor often encroaches on surrounding organization and adjacent viscera, the expression was pelvic wall and pelvic bottom peritoneum thickens, the boundary between bump and ovary and uterus was fused mutually, and the adipose gap of lesion periphery was fuzzy. After neoadjuvant chemotherapy, the mass completely subsided, no tumor cells attached to the surrounding tissues and organs of the original tumor, and the signs of tumor invasion were significantly improved. However, it was difficult to avoid tissue fibrosis caused by chemotherapy, which would make CT images show that the fat space between tissues and organs was still a flock-like or cord-like high-density shadow, thus misleading the examiner to judge this CT manifestation as tumor invading surrounding tissues and organs. In addition, the effect of neoadjuvant chemotherapy was not good in some patients, and the mass regression was not obvious. CT examination still showed signs of tumor invasion of surrounding tissues and organs.By reason of the foregoing, CT findings such as lymphadenectasis, degree of peritoneal thickening, ascites, and maximum length diameter of the mass have significance in appraising the efficacy of neoadjuvant chemotherapy for ovarian cancer, and CT could be used to evaluate the efficacy of neoadjuvant chemotherapy for ovarian cancer.


Selected drug candidates showed a reactive profile that was comparable to standard Dabrafenib. To characterize the stability of protein–ligand complexes, molecular dynamic simulations were performed, providing insight into the molecular interactions. The NEK7–Dabrafenib complex showed stability throughout the simulated trajectory. In addition, binding affinities, pIC50, and ADMET profiles of drug candidates were predicted using deep learning models. Deep learning models predicted the binding affinity of compound 762 best among all derivatives, which supports the findings of virtual screening. These findings suggest that top hits can serve as potential inhibitors of NEK7. Moreover, it is recommended to explore the inhibitory potential of identified hits compounds through in-vitro and in-vivo approaches.NIMA-related kinase7 (NEK7) plays a multifunctional role in cell division and NLRP3 inflammasone activation. A typical expression or any mutation in the genetic makeup of NEK7 leads to the development of cancer malignancies and fatal inflammatory disease, i.e., breast cancer, non-small cell lung cancer, gout, rheumatoid arthritis, and liver cirrhosis. Therefore, NEK7 is a promising target for drug development against various cancer malignancies. The combination of drug repurposing and structure-based virtual screening of large libraries of compounds has dramatically improved the development of anticancer drugs. The current study focused on the virtual screening of 1200 benzene sulphonamide derivatives retrieved from the PubChem database by selecting and docking validation of the crystal structure of NEK7 protein (PDB ID: 2WQN). The compounds library was subjected to virtual screening using Auto Dock Vina. The binding energies of screened compounds were compared to standard Dabrafenib. In particular, compound  Cancer is the most common cause of death, with a high mortality rate worldwide causing 10 million fatalities per year. Cancer is characterized by unregulated cell growth and rapid proliferation . Among aNEK7 is a highly conserved serine/threonine kinase consisting of approximately 302 amino acids . NEK7 isIn addition, NEK7 also encourages the proliferation of resting cells, which indicates its high-level involvement in various cancer types, including non-small lung cancer, breast cancer, NLRP3-related inflammatory disease, and gastric cancer progression . NEK7 alBesides promoting the proliferation of various resting cells, NEK7 is also involved in the progression and development of fatal inflammatory diseases, including Alzheimer’s disease, auto-immune disorders, inflammatory bowel disease, gout, and tumor formation . ResearcNEK7 is a promising target for multiple diseases, primarily cancer-related therapy research. NEK7 came into consideration two decades ago , but it Moreover, there is no FDA-approved medication that can selectively inhibit the expression of NEK7. Only Dabrafenib has shown activity against BRAF-mutant melanoma, which expresses more NEK9 . These fThe current study focused on structure-based virtual screening (SBVS) of 1200 compounds library and drug repurposing of FDA-approved drug Dabrafenib. Dabrafenib demonstrated inhibitory potential against NEK9 with an IC50 value ranging from 1–9 nM ,12. DabrClinically, sulphonamide-possessing drugs are used to treat lower urinary tract infections, whereas aromatic or hetero-aromatic sulphonamide derivatives possess a wide range of biological activities, including anti-tumor, anti-rheumatic, anti-microbial, and anti-inflammatory ,19,20,21The library of 1200 structural analogues of Dabrafenib was retrieved from the PubChem database and subjected to the in-silico drug discovery process. The discovery of a new anti-cancer agent is an extensive and laborious process. Thus, computer-aided drug design (CADD)  methods 50 values of top hits obtained via virtual screening. Predicted values of top hits were compared to in-vitro activity of Dabrafenib.The current study was focused to revisit the activity of Dabrafenib against NEK7 protein . In addiFurthermore, in-silico ADMET properties were also determined using a message-passing neural network (MPNN). The MPNN model is widely used for prediction of molecular properties such as blood brain barriers, human intestinal absorption, and solubility profiles . The molThis is the first comprehensive computational study for the identification of selective inhibitors of the NEK7 protein. The current study has utilized the latest computational approaches, suggesting identified hits as a new strategy for treatment of NEK7-associated malignancies. Findings of the current study suggest the exploration of the inhibiting potential of these hits at the molecular level using in-vitro and in-vivo experimental techniques.The ground state electronic energy is ascertained by electron density of the compound . The elehttps://pubchem.ncbi.nlm.nih.gov/) (accessed on 28 April 2022) to create the ligand library. There were 1200 structural analogues of Dabrafenib in the library. PyRx software was used to prepare the compounds library, which was converted to pdbqt format for virtual screening using Auto Dock Vina. The MMFF94 force field was used to minimize the energy of ligands. The crystallographic structure of the targeted protein was retrieved from Protein Data Bank (https://www.rcsb.org/) (accessed on 1 May 2022) (PDB ID: 2WQN). After that, MGL tools were used to prepare macromolecule, which included removing Het atoms and water molecules, and the addition of polar hydrogen. The protein was examined for any missing residues. Furthermore, Kollman’s charges were used to neutralize protein, and Gasteiger charges were calculated. Finally, for virtual screening of the compound library using Auto Dock Vina, a 1-angstrom grid box was built centered on the crystalline structure of protein at the point of co-crystal ligand (ADP) binding-site coordinates. The central xyz axis of the grid box was set to 80 × 80 × 80. Virtual screening was carried out after the targeted protein was prepared utilizing Auto Dock Vina’s script-based technique. The exhaustiveness was set to 5 and the number of nodes was set to 20. The virtual screening was repeated twice to ensure the accuracy of docking results. In addition, docking protocol was validated by re-docking the co-crystal ligand with targeted protein. A RMSD value of less than 2 angstrom indicates the reliability of the docking pose. After completion of virtual screening, the output findings of the virtual screening module were analyzed and docking scores of drug candidates were compared to standard Dabrafenib. Only four compounds were found to have higher docking scores than standard Dabrafenib. The top hits were subjected to further analysis using deep learning algorithms. Deep learning models were used to predict drug affinity and determine the stability of protein–ligand complexes.Drug candidates were retrieved from the PubChem database   was used50) and ADMET profile. Therefore, we have employed deep learning models to predict IC50, pIC50, and ADMET properties of top hits acquired through virtual screening in order to provide a direct comparison of binding affinities of top hits with standard Dabrafenib. Predicting the binding affinity and ADMET characteristics in silico, rather than using an experimental method, is a promising alternative. Deep learning (DP) models were used to predict drug target interactions (DTI) in the current work, which were formulated on encoder and decoder architectures. A DL model takes the SMILES string and amino acid sequence of the targeted protein as input and uses over 17 state-of-the-art DP learning techniques to predict drug efficacy indicators  yield increasingly reliable assessments of many characteristics . The B3LIn the present study, geometries of FDA-approved drug Dabrafenib and top hits were completely optimized using the DFT/B3LYP method and 6-31G* as a basis set. No negative frequencies were obtained after the geometry optimization, which demonstrates that current geometries are true local minima. Optimized structures of drug candidates are presented in 762 showed high value for polarizability and dipole moment, which indicates its high polarity and chemical reactivity. Optimization and polarizability values of top hits and Dabrafenib are given in The compound The way a molecule interacts with other species is determined by its frontier molecular orbitals. The highest occupied molecular orbital, or HOMO, is the outermost orbital-bearing electrons, and it primarily works as an electron donor. The lowest unoccupied molecular orbital, or LUMO, is the innermost orbital with free electron acceptor sites. The ionization potential ought to be proportional to the HOMO energy, while the LUMO energy should be proportional to the electron affinity .762 has the narrowest energy gap at 0.127 eV among all the compounds. Compound 248 has a greater energy gap, measuring 0.152 eV. Thus, it shows compound 762 is chemically more reactive than all other compounds, which are comparatively the stable ones. In addition, the electron density of HOMO orbitals for Dabrafenib was localized over morpholine and piperdinyl rings, whereas electron density of LUMO is localized to the carbonitrile and benzocarbazole moiety of the drug. FMOs orbitals are shown in The energy gap is the difference in energy between the HOMO and LUMO orbitals, and it is the most key variable in predicting the stability of a molecule. The HOMO–LUMO energy gap is used to evaluate the chemical reactivity and kinetic stability of the molecule. A soft molecule is a structure with a narrow gap that has a higher degree of polarization. As a measure of electron conductivity, the energy difference between HOMO and LUMO was recently employed to illustrate the bioactivity of intramolecular charge transfer (ICT). The stronger the chemical reactivity and the less stable the kinetics, the smaller the gap. The compound 248 presented greater energy gaps, indicating it to be the tougher among all compounds. Compound 208 demonstrated the greatest electrophilicity index value of 0.207 eV. This indicates that compound 208 is an excellent electrophile among all the other compounds. The HOMO–LUMO energy gap for Dabrafenib was found to be 0.159 eV. Dabrafenib showed a softness value of 6 ; Softness: S = 1/2η; Electronegativity: χ = −1/2(ELUMO + EHOMO); Chemical potential: µ = −χ; Electrophilicity index: ω = µ/2η.762 showed a maximum binding energy of −42.67 kJ/mol and exhibited strong binding affinity with NEK7 protein when subjected to the DL prediction model.Initially, the Molecular docking methodology was validated by redocking a co-crystal ligand with targeted protein using the same coordinates. RMSD values of less than 2 angstrom were obtained, which demonstrate the successful validation of the docking protocol and can be used to describe ligand poses with specificity and accuracy. Afterward, virtual screening was conducted with 1200 ligands library and Dabrafenib. The coordinates of co-crystal ligand were used to dock the ligand library and Dabrafenib. Out of 1200 drug candidates, only four drug candidates showed excellent binding energies that were even better than Dabrafenib. The binding energies of top hits and Dabrafenib are tabulated in 208 exhibited promising hydrophobic and hydrophilic interactions. The amino acid residues involved in important molecular interactions were as follows: ASP115, ARG121, GLY117, ASP179, PHE168, ILE195, ALA114, ALA61, ILE40, and ASP118. It was observed that two hydrogen bonds were involved in stabilizing the protein–ligand complex. One hydrogen bond was observed with ASP118 with a bond length of 2.97 angstrom, while the second hydrogen bond was observed with GLY117 having a bond length of 3.14 angstrom. Important residues of the active site were engaged in hydrophobic interactions, including van der Waals interactions, pi-alkyl and alkyl–alkyl interactions. The docking score of compound 208 was found to be −33.47 kJ/mol. Similarly, compound 248 exhibited stronger molecular interactions with the following amino acid residues: ARG121, ASP118, ALA165, LYS63, ASN166, ASP179, PHE168, LEU111, VAL48, ALA116, ASP115, and GLY117. It was discovered that important amino acid residues of the NEK7 protein’s DLG/DFG motifs were involved in interactions. Furthermore, the amino acids LEU111 and LYS163 interacted via hydrophobic bonds. Two important hydrogen bonds were contributing toward the stability of conformations. One hydrogen bond engaged GLY117 residues with a bond length of 2.2 angstroms. Another hydrogen bond was engaging ASN166 amino acid with a bond length of 3.34 angstroms. Among hydrophobic interactions, van der Waals interactions played a pivotal role in stabilizing the complex. The docking score of the compound 248 was −35.56 kJ/mol. The putative 2D and 3D binding modes of compounds 208 and 248 are shown in Among the four top hits, compound 255, which exhibited potent molecular interactions with amino acid residues of the active site. It was the second-best drug candidate obtained via virtual screening. Amino acid residues involved in bonding and nonbonding interactions were as follows: PHE45, SER46, LYS63, ALA114, ASP115, GLU112, PHE168, ASP179, VAL48, GLY43, and GLN44. It was observed that two important hydrogen bonds with short bond lengths were contributing toward stabilizing the complex. One hydrogen bond occurs between the electronegative oxygen atom of the compound 255 and the SER46 residue of the targeted protein. Moreover, the second hydrogen bond was engaged in GLY117 with a bond length of 3.16 angstroms. As shown in 255.Another important top hit was compound 762, It has shown excellent docking scores and demonstrated significant binding affinity obtained through deep learning models. It was observed that compound 762 was engaged in three hydrogen bonds of moderate-to-strong strength. One hydrogen bond occurred between the pentazole ring of the compound 762 and the electronegative oxygen atom of TYR201. The bond length of interaction was 3.08 angstroms. Similarly, the second hydrogen bond engaged SER234 residues with a surprisingly smaller bond length of 2.92 angstroms. These interactions lend enough testimony to stronger molecular interactions and more stabilized protein–ligand complexes. Furthermore, the third and last hydrogen bond occurred between TYR237 and compound 762 with a bond length of 3.02 angstroms. All three amino acid residues involved in hydrogen bonding belong to the activation loop of the NEK7 protein. The remaining active site residues, ILE123, GLU228, PHE236, MET203, PRO200, LEU246 and LEU232, engaged in hydrophobic interactions with compound 762. The docking score and binding affinity (IC50) were predicted to be best among all top hits, i.e., −42.67 kJ/mol and 61.74 nM, respectively. Compound 762 could be a promising drug candidate for the treatment of NEK7-associated malignancies. The binding interactions of compounds 255 and 762 are shown in Now referring to the top hit obtained through SBVS, namely compound The bonding and non-bonding interactions of standard Dabrafenib was involving important amino acid residues of NEK7 activation loop. ARG50, LYS38, ALA165, ILE40, GLY117, ASP115, PHE168, LEU111, LEU112, ALA114, LEU113, ALA161, ASP179, and ILE95 were the amino acid residues implicated in molecular interactions with Dabrafenib. Dabrafenib exhibited significant molecular interactions, which contributed towards complex binding affinity. The strong interactions were observed with targeted protein and sulphonamide rings. The sulphonamide ring was implicated in several important stabilizing contacts, including conventional hydrogen bonding with ASP115 of the activation loop, Pi-cation interaction with ARG50, and interactions with ILE40 and ASP115 by two fluorine atoms connected to the ring. PHE168 formed pi-cation and pi-pi T-shaped contacts with the butylthiazole ring, whereas the pyrimidine ring produced conventional hydrogen bonds with GLU112 and ASP179, a carbon–hydrogen connection with ALA114, and a pi-alkyl interaction with ALA161. Due to important chemical interactions, Dabrafenib has a good binding energy of −33.89 kJ/mol. van der Walls interactions are essential hydrophobic interactions that have been observed with the amino acids LYS38, ALA165, GLY117, LEU111, LEU113, and ILE95. 248, 255 and 762, electronegative oxygen atoms were involved in stronger intermolecular interactions. In contrast, nitrogen atom was involved in hydrogen bonding by donating the hydrogen bond for example, in case of compound 208, nitrogen was donating hydrogen bond to ASP118 residue. In addition, the docked conformation of ligands on the protein surface is also represented by different colored regions  map is a key activity in drug design as it determines the chemical reactivity of the compound and its ability to produce important molecular interactions. It is an effective way to visualize the molecular reactivity and evaluate the nature of ligand-binding with a targeted protein. The ESP map is depicted by different colored regions depending upon the electronegativity of the compound. The highly electronegative part is represented by the color red, whereas the electropositive part is represented by the color blue. The QM calculations were performed using DFTs at the B3LYP/6-31G* level of theory.  regions . The reddr_sasa) [208, 248, 255, and 762) were subjected to the calculation of BSA. It was observed that the targeted NEK7 protein was buried up to 80% and 70% by compounds 208 and 248, respectively. In particular, amino acid residues ILE40 and PHE168 were strongly buried by compound 208 (49 Å2). Compound 248, on the other hand, was strongly engaging the ARG121 and PHE168 with BSA of 39 Å2 and 41.3 Å2 respectively. The detailed buried surface area of both compounds is shown in Molecular interactions are the critical factors in determining the stability of protein–ligand complexes. Molecular interactions existing between protein–ligand complexes can be modelled by taking into account the physicochemical properties and complementarity of the shape of the binding interface. A useful method for determining the complementarity of the shape and extent of molecular interactions is the estimation of the buried surface area (BSA) of a protein–ligand complex. In the current study, the BSA of best complexes was calculated using a new Shrake–Ruply algorithm-based tool (dr_sasa)  used for255 and 762, it was observed that both compounds significantly engaged the amino acid residues of the target protein. Compound 255, in particular, was burying the surface area of the NEK7 protein by up to 360 Å2. The BSA of compound 255 with VAL48, LYS63, ALA114, and PHE168 was 168, 212, 187, and 351 Å2, respectively, which was the best among all top hits. These values demonstrate the strong nature of molecular interactions existing between the target protein and compound 255. In the case of compound 762, important amino acid residues were buried by compound 762. In particular, TYR201, TYR237, and MET241 were significantly buried by compound 762 with BSA of 64, 72, and 25.6 Å2. Moreover, it was worth noticing that the major contributing atoms were oxygen, nitrogen, fluorine, sulphur, and chlorine, which were involved in increasing the contact surface area of compounds with a targeted protein. The BSA of compounds 255 and 762 is shown in In terms of compounds The molecular docking technique is comparatively rapid and imprecise. The docking deficiencies and flexibility of protein may interfere with protein–ligand complex. However, molecular dynamic simulations are computationally expensive and time-consuming but provide reliable and accurate illustration of protein displacement. Considering these facts, molecular dynamic simulations were undertaken using Desmond software package ,59. The The RMSD patterns for C-alpha atoms of NEK7 protein were estimated in order to determine the effect of the bounded drug on the conformational stability of NEK7 protein. These findings suggest that the ligands stayed firmly bound to the receptor throughout the simulation period. Moreover, small RMSD patterns indicate the fewer structural re-arrangements and lesser conformational changes within binding site residues .It is beneficial to identify local differences in the protein chain by using the root-mean-square fluctuation (RMSF). The contact profiles of NEK7–Dabrafenib were computed from simulated trajectories, as shown in The hydrogen bond with ALA114 existed for more than 25% of simulation time. Hydrophobic interactions existed between VAL48, LEU113, VAL48, and PHE168. These molecular interactions contributed towards stabilizing the protein–ligand complex.Radius of gyration (Rg) is measure of protein compactness, stability, integrity and foldness of protein backbone. The Rg trajectory for NEK7 is depicted in 2, which is quite acceptable. The average SASA value was computed to be 282.72 Å2 (Solvent-accessible surface area (SASA) is the area of protein that is accessible by the solvent. The higher the value for SASA, the lower the stability of the protein. In the current study, residue wise SASA was calculated and ranged between 180 to 350 Å82.72 Å2 . The resIt is an essential multivariate statistical technique used to describe the protein dynamics in a spatial scales. It is a linear relationship that extracts essential features of protein using covariance and/or correlation matrices. These matrices are derived from the atomic coordinate that represents the accessible degree of freedom (DOF) of the protein in a simulated trajectory. In the current study, Pearson’s cross-correlation matrix was employed as it can normalize the large protein variables and prevent high atomic variations that can skew the results. In addition, eigenvectors with a specific variance value also play an important role in characterizing the motion of protein in spatial scales. In the current study, essential dynamics of protein were calculated by applying PCA analysis to the protein trajectory. It was observed that different variables were forming tight clusters with narrow angles, which indicates that they were correlated with vectors (PCs) . PCs areCorrelation matrices are also the correlation coefficients between variables and PCs. In Pearson’s cross-correlation, the percent of variance in a protein variable is explained by PCs. Molecular docking is a robust technique for determining the binding orientation of a protein–ligand complex. However, it is still lacking in its ability to correctly identify the binding affinities of docked ligands. In order to determine correct binding energies of docked conformations, MM-GBSA energy calculations were performed, which are an efficient and reliable method for the determination of binding free energies. The MM-GBSA method provides free energy calculations by taking into account all hydrophobic, hydrophilic and electrostatic interactions . After eThe MM-GBSA energies for the protein–ligand complex was determined through the Thermal_mmgbsa script of Schrodinger. MM-GBSA energies are tabulated in bnd I refers to bond energy, Eel refers to electrostatic energy and EvdW represents van der Waals interactions. In the current study, Poisson–Boltzmann calculations were performed using the internal PBSA solver in mmpbsa_py_energy. All units are represented in kcal/mol. MM-PBSA energy analysis is given in In MMPBSA energy analysis, the free binding energies of protein, ligand and protein–ligand complex are estimated by following equation;762 showed the lowest clinical toxicity value of 0.28%. The ADMET profile of top hits is tabulated in In-silico ADMET properties of top-ranked hits were determined by deep learning models; more than 17 models were employed at the backend, which provided predictions on the ADME profile of each hit. It is an important part in drug development that can identify the desired pharmacological properties of compounds. In the current study, the message passing neural network (MPNN) is employed for the determination of ADMET properties. It was observed that compound 50, and ADMET properties. It was observed that compound 762 showed good binding affinity and demonstrated a promising ADMET profile. Moreover, molecular dynamics simulations were performed to determine the stability of the protein–ligand complex under accelerated conditions. It was observed that the ligand remained significantly attached to the protein-activation loop, suggesting potential inhibiting activity of the compound. In short, the findings of the current study identify top hits that could prove an effective treatment strategy for NEK7-associated cancer malignancies. These findings will assist researchers to develop newer leads without consuming much time and money. Further experimental studies are also recommended for future prospects.In the current study, structure-based virtual screening of a 1200-compound library and Dabrafenib was carried out using Auto Dock Vina. These compounds are in the early stages of drug development, and the in-silico approach used in this study was contributing toward investigating the inhibiting potential of these compounds through molecular docking, DFTs, and MD simulation, as well as determining the drug-like properties of these compounds through deep learning models. The FDA-approved drug, Dabrafenib, was considered as a standard drug to which in-silico findings could be compared. SBVS findings discovered four important hits having better binding energies as compared to standard Dabrafenib. In addition, the chemical reactivity profiles of top hits were determined via DFT studies. Findings from DFT studies revealed the reactive nature of the compounds. Moreover, the current study has utilized deep learning models for prediction of binding affinity, pIC