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The Coronavirus disease 2019 is a disease caused by the etiological agent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was a newly identified β-coronavirus that had spread rapidly around the world, resulting in a very high number of infections and deaths, threatening public security, and placing a heavy burden on global healthcare systems and economy. The present study was done to study antibody response in COVID-19 infection.: The study was carried out in the Department of Respiratory Medicine, R.D. Gardi Medical College, Ujjain, India after approval by ethics committee. The post COVID-19 cases attending respiratory medicine OPD were included in the study.: The present study was conducted in 122 patients. The mean age of the patients was 51.20±14.21 years, median age was 54.5 years, the minimum age was 14 years and the maximum age was 80 years, the most number of COVID-19 infection cases was in the elderly age group of more than 50 years. The severity of disease was seen in those above 50 years of age (57.3%) with p>0.05. Among the severe cases mean age of the cases was 49±17 years, in moderate cases mean age 52±14 and among mild group of cases mean age was 51±15 years.In the present study there were 73.8% males and females were 26.2% Among COVID-19 cases who reported in the OPD the maximum that is 86 (>70%) patients had moderately severe disease, while severe cases were only about 14 %. Mild cases were 15.6%.In the present study RT-PCR were negative in 36(29.5%) as against 86 (70.5%) were confirmed cases of COVID-19 and among positive cases 40 % cases became negative between 6-10 daysAmong severe disease cases mean SARS COV2 antibody 650.06±240.47 was significantly higher as compare to moderate cases 461.49±331.41 and mild cases 404.11±240.47.: India has the world’s largest vaccination program. COVID-19 antibodies estimation are important tests carried out to determine the response of the body against COVID-19 infection.Our results show a significant association between SARS COV2 antibody and the severity of disease.

Purpose Examine the effectiveness of interventions to approach guideline-adherent surgical referrals for low back pain assessed via systematic review and meta-analysis. Methods Five databases (10 September 2021), Google Scholar, reference lists of relevant systematic reviews were searched and forward and backward citation tracking of included studies were implemented. Randomised controlled/clinical trials in adults with low back pain of interventions to optimise surgery rates or referrals to surgery or secondary referral were included. Bias was assessed using the Cochrane ROB2 tool and evidence certainty via Grading of Recommendations Assessment, Development and Evaluation (GRADE). A random effects meta-analysis with a Paule Mandel estimator plus Hartung–Knapp–Sidik–Jonkman method was used to calculate the odds ratio and 95% confidence interval, respectively. Results Of 886 records, 6 studies were included (N = 258,329) participants; cluster sizes ranged from 4 to 54. Five studies were rated as low risk of bias and one as having some concerns. Two studies reporting spine surgery referral or rates could only be pooled via combination of p values and gave evidence for a reduction (p = 0.021, Fisher’s method, risk of bias: low). This did not persist with sensitivity analysis (p = 0.053). For secondary referral, meta-analysis revealed a non-significant odds ratio of 1.07 (95% CI [0.55, 2.06], I2 = 73.0%, n = 4 studies, Grading of Recommendations Assessment, Development and Evaluation [GRADE] evidence certainty: very low). Conclusion Few RCTs exist for interventions to improve guideline-adherent spine surgery rates or referral. Clinician education in isolation may not be effective. Future RCTs should consider organisational and/or policy level interventions. PROSPERO registration CRD42020215137.

Adjacent segment disease (ASD) is a potential complication following lumbar spinal fusion.This study aimed to demonstrate the demographic, clinical, and operative risk factors associated with ASD development following lumbar fusion.Systematic review and meta-analysis.We identified 35 studies that reported risk factors for ASD, with a total number of 7,374 patients who had lumbar spine fusion.We investigated the demographic, clinical, and operative risk factors for ASD after lumbar fusion.A literature search was done using PubMed, Embase, Medline, Scopus, and the Cochrane library databases from inception to December 2019. The methodological index for non-randomized studies (MINORS) criteria was used to assess the methodological quality of the included studies. A meta-analysis was done to calculate the odds ratio (OR) with the 95% confidence interval (CI) for dichotomous data and mean difference (MD) with 95% CI for continuous data.Thirty-five studies were included in the qualitative analysis, and 22 studies were included in the meta-analyses. The mean quality score based on the MINORS criteria was 12.4±1.9 (range, 8-16) points. Significant risk factors included higher preoperative body mass index (BMI) (mean difference [MD]=1.97 kg/mThe overall level of evidence was low to very low. Higher preoperative BMI, floating fusion, superior facet joint violation, and decompression outside fusion construct are significant risk factors of development of ASD following lumbar fusion surgeries.

Aims Intraoperative pressure sensors allow surgeons to quantify soft-tissue balance during total knee arthroplasty (TKA). The aim of this study was to determine whether using sensors to achieve soft-tissue balance was more effective than manual balancing in improving outcomes in TKA. Methods A multicentre randomized trial compared the outcomes of sensor balancing (SB) with manual balancing (MB) in 250 patients (285 TKAs). The primary outcome measure was the mean difference in the four Knee injury and Osteoarthritis Outcome Score subscales (ΔKOOS4) in the two groups, comparing the preoperative and two-year scores. Secondary outcomes included intraoperative balance data, additional patient-reported outcome measures (PROMs), and functional measures. Results There was no significant difference in ΔKOOS4 between the two groups at two years (mean difference 0.4 points (95% confidence interval (CI) -4.6 to 5.4); p = 0.869), and multiple regression found that SB was not associated with a significant ΔKOOS4 (0.2-point increase (95% CI -5.1 to 4.6); p = 0.924). There were no significant differences between groups in other PROMs. Six-minute walking distance was significantly increased in the SB group (mean difference 29 metres; p = 0.015). Four-times as many TKAs were unbalanced in the MB group (36.8% MB vs 9.4% SB; p < 0.001). Irrespective of group assignment, no differences were found in any PROM when increasing ICPD thresholds defined balance. Conclusion Despite improved quantitative soft-tissue balance, the use of sensors intraoperatively did not differentially improve the clinical or functional outcomes two years after TKA. These results question whether a more precisely balanced TKA that is guided by sensor data, and often achieved by more balancing interventions, will ultimately have a significant effect on clinical outcomes. Cite this article: Bone Joint J 2022;104-B(5):604–612.

BACKGROUND Routine check-ups for adolescent idiopathic scoliosis are critical to monitor progression and prescribe interventions. AIS is primarily screened via physical examination. If there are features of deformity, radiographs are necessary for diagnosis or follow-up, guiding further management, i.e., bracing corrections for moderate deformities and spine surgeries for severe deformities. However, this subjects children to repetitive radiation and routine practices can be disturbed. OBJECTIVE We aim to develop and prospectively validate an open mobile platform powered by validated deep learning models known as ScolioNets, for AIS severity and curve type classifications as well as progression identifications, to facilitate timely management of AIS with no extra radiation exposure. METHODS During the technology development stage, ScolioNets was trained and validated by 1780 back photos, consisting of heterogeneous severities and curve types. The ground truth (GT) labels for severity (follow-up vs. considering surgery), curve type (thoracic, thoracolumbar/lumbar, or mixed curve types) and progression (progress vs. non-grogress) were manually annotated by experienced deformity surgeons based on the radiographic examinations of the participants’ spines. Further prospective testing on 378 patients was conducted after the validated ScolioNets was deployed on our open platform AlignProCARE. In addition to comparing with the GTs, the results were also compared with two spine surgeons’ assessments of nude back appearance, while they were blinded to the GT measurements. Quantitative statistical analyses were performed to assess the performance of the open platform in classifying the deformity as well as in distinguishing progression during follow-up. RESULTS The platform recommended follow-up or surgery with the area under curves (AUCs) of receiver operating characteristic curves being 0.839, and 0.902, while distinguishing among thoracic, thoracolumbar/lumbar, or mixed curve types with AUCs of 0.777, 0.760 and 0.860 respectively. For follow-ups, we distinguished subjects with or without curve progression with an AUC of 0.757. All results were comparable with the senior surgeon’s assessment results and superior to the junior spine surgeon’s results. CONCLUSIONS Our open platform has the potential for out-of-hospital accessible and radiation-free applications in managing children with AIS, with comparable performance as a spine surgeon experienced in AIS management.

Routine check-ups for adolescent idiopathic scoliosis are critical to monitor progression and prescribe interventions. AIS is primarily screened via physical examination. If there are features of deformity, radiographs are necessary for diagnosis or follow-up, guiding further management, i.e., bracing for moderate deformity and surgery for severe. However, this subjects children to repetitive radiation and routine practices can be disturbed. Here, we demonstrate a mobile platform powered by ScolioNet, being trained and validated by 1780 back photos with paired radiographs, consisting of heterogeneous severities and curve types, manually annotated by experienced deformity surgeons. In further independent testing on 378 patients, the platform recommended follow-up or surgery with area under curves (AUCs) of receiver operating characteristic curves being 0.839, and 0.902, while distinguishing among thoracic, thoracolumbar/lumbar, or mixed curve types with AUCs of 0.777, 0.760 and 0.860 respectively. Our open platform has potential for out-of-hospital accessible applications in managing children with AIS.