The impact of fibromyalgia syndrome on obstructive sleep apnea syndrome in terms of pain threshold, daytime symptoms, anxiety, depression, disease severity, and sleep quality: a polysomnographic study

BackgroundCurrent studies have focused on the association of fibromyalgia syndrome (FMS) and obsctructive sleep apnea syndrome (OSAS). Results of these studies on the effect of this association have been inconsistent. The current study aimed to investigate the effect of FMS on OSAS regarding sleep quality, pressure pain threshold, fatigue, daytime symptoms, anxiety, and depression, and also to determine the relationship between OSAS severity and FMS.MethodsIn a cross-sectional design, patients diagnosed with OSAS were evaluated in two groups comparing those with and those without FMS. Data on demographics, headache, morning fatigue, and chronic pain duration were collected. Questionnaires including the Fatigue Severity Scale (FSS), Fibromyalgia Impact Questionnaire (FIQ), Beck Depression Inventory (BDI), and Beck Anxiety Inventory (BAI) were completed. Pressure pain threshold, tender points, and polysomnographic data were recorded.ResultsOf 69 patients, 27 were diagnosed with FMS + OSAS and 42 were diagnosed as OSAS only. Statistically significant differences were found between the two groups in VAS, pain duration, morning fatigue, headache, BAI, tender point count, FIQ and FSS scores, and algometer measurements. All polysomnografic data were compared, and no statistically significant differences were found between the two groups. There were no statistically significant differences in the algometer, BDI, BAI, FIQ, and FSS scores when analyzed according to the severity of OSAS.ConclusionThe findings suggest that FMS has no effect on polysomnographic parameters of OSAS. Headache, daytime fatigue, anxiety, depression, pain duration, and pain intensity are higher while the pressure pain threshold is lower when FMS is present. No correlation was found between OSAS severity and FMS, fatigue, pressure pain threshold, depression, and anxiety.Clinical Trial Registration Number: NCT05367167/date: April 8, 2022

Slaughter age and carcass traits of pheasants

WOS: 000082977900007This study was conducted to determine growth performance and carcass traits of pheasants at different slaughter ages. Day-old male and female chicks of a native pheasant genotype were used in the trial. The pheasants were grown in a floor rearing system. Birds were slaughtered at 13, 14 and 15 weeks of age. The differences between the live weights of male and female pheasants were found to be significant (P < 0.05) at all slaughter ages. The average live weights of male and female birds combined were 868.69 +/- 26.78, 927.19 +/- 34.59 and 982.87 +/- 42.59 g for the 13th, 14th and 15th week of age, respectively. Feed efficiency was recorded as 4.60, 4.88 and 5.14 for the same slaughtering ages. The slaughter yield and the percentages of breast, thighs and edible internal organs were determined at the 13th, 14th and 15th week of age as 74.19%, 73.13% and 74.30%; 35.90%, 35.85% and 35.55%; 30.12%, 30.97 and 29.13%; 5.93%, 5.83% and 6.03%, respectively

Hyperglycemia as a risk factor for postoperative early wound infection after bicondylar tibial plateau fractures: Determining a predictive model based on four methods

Objectives: Identify a glucose threshold that would put patients with isolated bicondylar tibial plateau fractures at risk of early wound infection (i.e. less than 90 days). Design: Retrospective review of medical records. Setting: Academic American College of Surgeons (ACS) Level 1 trauma center. Patients: Adult patients between 2010 and 2015 with an operatively treated isolated bicondylar tibial plateau fracture and at least three glucose measurements during their hospitalization. Main Outcome Measurement: To predict infection using four different methods: maximum preoperative blood glucose (PBG), maximum blood glucose (MGB), Hyperglycemic Index (HGI), and Time-Weighted Average Glucose (TWAG). Results: 126/381 patients met our inclusion criteria. Fifteen (12%) patients had an open fracture and 30/126 (23%) developed an infection. Median glucose for each predictive method studied was 114 (IQR 101.2–137.8) mg/dL for PBG, 144 (IQR 119–169.8) mg/dL for MBG, 0.8 (IQR 0.20–1.60) mmol/L for HGI, and 120.4 (IQR 106.0–135.6) mg/dL for TWAG. As expected, infected patients had higher PBG, MGB, and TWAG. HGI was similar in both groups. None of these differences prove to be statistically significant (p > .05). Logistic regression models for all the methods showed that having an open fracture was the strongest predictor of infection. Conclusion: It is well known that stress-induced hyperglycemia increases the risk of infection, we present and compare four models that have been used in other medical fields. In our study, none of the methods presented identified a glucose threshold that would increase the risk of infection in patients with bicondylar tibial plateau fractures. Level of Evidence: Retrospective review, Level III. See Instructions for Authors for a complete description of levels of evidence. © 2019 Elsevier Lt

Hyperglycemia as a risk factor for postoperative early wound infection after bicondylar tibial plateau fractures: Determining a predictive model based on four methods

Objectives: Identify a glucose threshold that would put patients with isolated bicondylar tibial plateau fractures at risk of early wound infection (i.e. less than 90 days). Design: Retrospective review of medical records. Setting: Academic American College of Surgeons (ACS) Level 1 trauma center. Patients: Adult patients between 2010 and 2015 with an operatively treated isolated bicondylar tibial plateau fracture and at least three glucose measurements during their hospitalization. Main Outcome Measurement: To predict infection using four different methods: maximum preoperative blood glucose (PBG), maximum blood glucose (MGB), Hyperglycemic Index (HGI), and Time-Weighted Average Glucose (TWAG). Results: 126/381 patients met our inclusion criteria. Fifteen (12%) patients had an open fracture and 30/126 (23%) developed an infection. Median glucose for each predictive method studied was 114 (IQR 101.2–137.8) mg/dL for PBG, 144 (IQR 119–169.8) mg/dL for MBG, 0.8 (IQR 0.20–1.60) mmol/L for HGI, and 120.4 (IQR 106.0–135.6) mg/dL for TWAG. As expected, infected patients had higher PBG, MGB, and TWAG. HGI was similar in both groups. None of these differences prove to be statistically significant (p > .05). Logistic regression models for all the methods showed that having an open fracture was the strongest predictor of infection. Conclusion: It is well known that stress-induced hyperglycemia increases the risk of infection, we present and compare four models that have been used in other medical fields. In our study, none of the methods presented identified a glucose threshold that would increase the risk of infection in patients with bicondylar tibial plateau fractures. Level of Evidence: Retrospective review, Level III. See Instructions for Authors for a complete description of levels of evidence. © 2019 Elsevier Lt