后外侧结构重建在新鲜股骨颈骨折人工股骨头置换术中的应用

目的探讨后外侧结构重建对后外侧入路人工股骨头置换术术后早期关节脱位的影响。方法选取2016年9月至2017年8月于我院行后外侧入路初次人工股骨头置换术的股骨颈骨折患者60例,根据术中是否修补关节囊及外旋肌群分为重建组(33例:舌形切开关节囊,术中将关节囊及外旋肌群原位缝合在大转子后方及臀中肌肌腱附着处)和对照组(27例:切除关节囊后,术中未进行外旋肌群修复重建)。比较两组的手术情况及术后近期关节功能情况。结果重建组的手术时间为(45.0±15.3) min,长于对照组的(35.0±12.4) min (P <0.05)。重建组术腔引流量为(200.0±80.0) m L,少于对照组的(420.0±120.6) m L (P <0.05)。重建组的早期脱位率为0.000%(0例),与对照组的7.407%(2例)比较无统计学差异(P>0.05)。重建组术后Harris评分为(92.0±3.4)分,高于对照组的(88.2±5.0)分(P <0.05)。结论在后外侧入路人工股骨头置换过程中行后外侧结构重建能够有效减少术腔引流量,提高髋关节Harris评分,对维持髋关节软组织平衡具有重要意义。     

早期十全大补汤联合肠内营养乳剂治疗胃癌术后(气血两虚证)喂养不耐受临床评价

目的探讨早期使用十全大补汤联合肠内营养乳剂(TP)治疗胃癌术后(气血两虚证)发生喂养不耐受(FI)的影响因素及对营养指标、中医证候积分的影响。方法回顾性分析术后早期行十全大补汤联合TP治疗的80例胃癌术后(气血两虚)患者的病历资料,根据是否出现FI分为耐受组(34例)和不耐受组(46例)。FI的相关影响因素进行单因素及多因素分析,并观察FI对患者营养指标、中医证候积分的影响。结果单因素分析显示,患者术后第1天下床活动时间、开始肠内营养(EN)的时间、使用营养泵、早期灌肠与FI的发生密切相关(P <0. 05);多因素Logistic回归分析显示,患者第1天下床活动时间≥2 h[OR=0. 022,P=0. 001,95%CI(0. 002,0. 223)]、使用营养泵[OR=0. 021,P=0. 000,95%CI(0. 003,0. 162)]是FI发生的独立危险因素;术后10 d,耐受组患者白蛋白(ALB)、血红蛋白(Hb)升高水平优于不耐受组(P <0. 05),中医证候积分显著低于不耐受组(P <0. 05)。结论胃癌术后(气血两虚证)患者早期给予十全大补汤联合TP治疗开始后,患者第1天下床活动时间不短于2 h、使用营养泵能有效减少FI的发生,并改善了患者的营养状态,减轻了中医临床症状。     

PDCA循环在消化道肿瘤伴糖尿病患者营养全程管理中的效果观察

目的 探讨PDCA 循环在消化道肿瘤伴糖尿病患者营养全程管理中的效果观察。方法 运用PDCA循环对80例不同程度营养不良的消化道肿瘤伴糖尿病患者实施营养全程管理,比较干预前后患者的体重、BMI指数、糖化血红蛋白、血红蛋白、总蛋白、白蛋白及球蛋白水平。结果 患者血红蛋白、总蛋白、白蛋白及球蛋白水平均有提高,比较差异均有统计学意义(P＜0.05)。结论 PDCA循环管理在消化道肿瘤伴糖尿病患者营养全程管理中有较大的优势, 值得推广。     

早发型子痫前期应用低分子肝素期待治疗的临床效果分析

目的分析早发型子痫前期应用低分子肝素期待治疗的临床效果。方法回顾分析2017-01—2018-12间在郑州大学第一附属医院产科终止妊娠的95例早发型子痫前期患者的临床资料。按终止妊娠前是否应用低分子肝素分为2组。对照组(47例)给予降压、解痉等治疗;观察组(48例)在对照组基础上加用低分子肝素。结果2组分娩孕周、妊娠延长时间、妊娠并发症发生率、新生儿出生体质量、新生儿窒息及胎儿宫内窘迫发生率、新生儿Apgar评分等,差异均无统计学意义(P>0.05)。结论对早发型子痫前期患者在常规治疗基础上短期应用低分子肝素,不能延长妊娠时间,不改善母婴结局。     

Prevalence and pathophysiology of early dumping in patients after primary Roux-en-Y gastric bypass during a mixed-meal tolerance test

Background

Early dumping is a poorly defined and incompletely understood complication after Roux-en-Y gastric (RYGB).

Egg Intake Has No Adverse Association With Blood Lipids Or Glucose In Adolescent Girls

Objective: Longitudinal data on cardiometabolic effects of egg intake during adolescence are lacking. The current analyses aim to evaluate the impact of usual adolescent egg consumption on lipid levels, fasting glucose, and insulin resistance during late adolescence (age 17–20?years).

Methods: Data from 1392 girls, aged 9 to 10 at baseline and followed for 10?years, in the National Heart, Lung, and Blood Institute’s National Growth and Health Study were used to examine the association between usual egg intake alone and in combination with other healthy lifestyle factors and late adolescent lipid levels, fasting glucose, and insulin resistance, measured as homeostasis model assessment of insulin resistance (HOMA-IR). Diet was assessed using 3-day food records during eight examination cycles. Girls were classified according to usual weekly egg intake, ages 9–17?years:?<1 egg/wk (n?=?361), 1 to <3 eggs/wk (n?=?703), and ≥3 eggs/wk (n?=?328). Analysis of covariance modeling was used to control for confounding by other behavioral and biological risk factors.

Results: Girls with low, moderate, and high egg intakes had adjusted low-density lipoprotein cholesterol levels of 99.7, 98.8, and 95.5 mg/dL, respectively (p?=?0.0778). In combination with higher intakes of fiber, dairy, or fruits and vegetables, these beneficial effects were stronger and statistically significant. There was no evidence that ≥3 eggs/wk had an adverse effect on lipids, glucose, or HOMA-IR. More active girls who consumed ≥3 eggs/wk had the lowest levels of insulin resistance.

Conclusion: These results suggest that eggs may be included as part of a healthy adolescent diet without adverse effects on glucose, lipid levels, or insulin resistance.     

Application of metagenomic next-generation sequencing for suspected infected pancreatic necrosis

BackgroundMetagenomic next-generation sequencing (mNGS) is increasingly used for the clinical diagnosis of infectious diseases, but there is a paucity of data regarding the application of mNGS in the early diagnosis of infected pancreatic necrosis (IPN).ObjectiveTo investigate the clinical application value of mNGS in the pathogenic diagnosis of IPN.MethodsForty-two patients with suspected IPN were prospectively and consecutively enrolled from August 2019 to August 2021. Blood samples were collected for mNGS and microbial culture simultaneously during fever (T ≥ 38.5 °C). For patients who had indications of surgical interventions, peri-pancreatic specimens were collected for mNGS and microbial culture simultaneously during the first surgical intervention to confirm IPN. The clinical performance of mNGS and microbial culture were compared.ResultsA total of 21 patients (50.0%) were confirmed to have IPN during hospitalization. The sensitivity of blood mNGS was significantly higher than blood culture (95.2% vs. 23.8%, P < 0.001) in diagnosing IPN. The negative predictive value of blood mNGS was 90.0%. The turnaround time of mNGS was significantly shorter than that of microbial culture [(37.70 ± 1.44) vs. (115.23 ± 8.79) h, P < 0.01] and the average costs of mNGS accounted for 1.7% of the average total cost of hospitalization. The survival analysis demonstrates that the positive blood mNGS result was not associated with increased mortality (P = 0.119).ConclusionsWith more valuable diagnostic performance and shorter turnaround time, clinical mNGS represents a potential step forward in the early diagnosis of IPN.     

Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) has currently caused the mortality of millions of people around the world. Aside from the direct mortality from the COVID-19, the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients. Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality, which did not relate to COVID-19 infection. It has in fact increased the risk of death in cardiovascular disease (CVD) patients. For this purpose, it is dramatically inevitable to monitor CVD patients’ vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death. Internet of things (IoT) and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’ data. The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments. To this end, this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments. Experimental results showed that the proposed method was able to detect cardiovascular events with better performance (95.30% average sensitivity and 95.94% mean prediction values).