UNASSIGNED: The aim of this study was to evaluate if the operated knee environment remains abnormal in patients successfully treated with anterior cruciate ligament reconstruction (ACL-R).
UNASSIGNED: Thirty asymptomatic patients were enrolled (28 men, 2 women, age 28.6 ± 6.54 years, body mass index: 24.9 ± 3.0 kg/m2) and evaluated at 42.2 ± 12.5 months after surgery. Patients were assessed with patient-reported outcome measurements and with a triaxial accelerometer. The temperature of the knees as well as four regions of interest were evaluated with an infrared thermographic camera FLIR T1020 (FLIR® Systems) according to a standardised protocol including a baseline evaluation and further evaluations immediately after exercise and after 5, 10 and 20 min. The temperature of the ACL-R knee was compared to that of the contralateral healthy knee for the purpose of the study.
UNASSIGNED: The mean temperature of the knee was higher (p = 0.010) for the ACL-R knees (31.4 ± 1.4°C) compared to the healthy knees (31.1 ± 1.6°C), as well as for the patellar area (p = 0.005), the lateral area (p = 0.016) and the medial area (p = 0.014). The analysis of the response to the exercises of the ACL-R knees showed similar trends to the healthy knees but higher temperature values at all time points (p < 0.05). Patients who underwent ACL-R with concomitant meniscal treatment showed higher knee temperatures compared to ACL-R knees without concomitant meniscal treatment after 5 (p = 0.047), 10 (p = 0.027) and 20 min (p = 0.048).
UNASSIGNED: The temperature of asymptomatic knees previously treated with ACL-R is higher than the contralateral healthy knee, both at rest and after exercise, with a further increase in knees that underwent both ACL-R and meniscal treatment. These results suggest an inflammatory state persisting years after the surgery, which could predispose to the early onset of knee degeneration.
UNASSIGNED: III, Case-control study.

Infrared thermography is considered a useful technique for diagnosing several skin pathologies but it has not been widely adopted mainly due to its high cost. Here, we investigate the feasibility of using low-cost infrared cameras with microbolometer technology for detecting skin cancer. For this purpose, we collected infrared data from volunteer subjects using a high-cost/high-quality infrared camera. We propose a degradation model to assess the use of lower-cost imagers in such a task. The degradation model was validated by mimicking video acquisition with the low-cost cameras, using data originally captured with a medium-cost camera. The outcome of the proposed model was then compared with the infrared video obtained with actual cameras, achieving an average Pearson correlation coefficient of more than 0.9271. Therefore, the model successfully transfers the behavior of cameras with poorer characteristics to videos acquired with higher-quality cameras. Using the proposed model, we simulated the acquisition of patient data with three different lower-cost cameras, namely, Xenics Gobi-640, Opgal Therm-App, and Seek Thermal CompactPRO. The degraded data were used to evaluate the performance of a skin cancer detection algorithm. The Xenics and Opgal cameras achieved accuracies of 84.33% and 84.20%, respectively, and sensitivities of 83.03% and 83.23%, respectively. These values closely matched those from the non-degraded data, indicating that employing these lower-cost cameras is appropriate for skin cancer detection. The Seek camera achieved an accuracy of 82.13% and a sensitivity of 79.77%. Based on these results, we conclude that this camera is appropriate for less critical applications.

UNASSIGNED: Diabetic peripheral neuropathy (DPN) affects patients\' quality of life significantly. To date, selecting the appropriate treatment remains challenging. While electroacupuncture (EA) has shown promise as an effective adjunct therapy for DPN, and infrared thermography (IRT) has been considered as a potential predictor of treatment efficacy, the evidence for both remains inconclusive. As such, the objectives of this trial are twofold: to ascertain the efficacy of EA for DPN, and to explore the feasibility of IRT as an adjunctive objective tool for efficacy assessment.
UNASSIGNED: The study was designed as a randomized, parallel, controlled trial. It spanned over 6 weeks of treatment and an additional 4 weeks of follow-up. 104 eligible participants will be stratified for severity of disease: mild with Toronto clinical scoring system(TCSS) score 6-8, moderate (TCSS score 9-11), and severe (TCSS score 12-19), and each level will be randomised in a 1:1 ratio into a EA group and waiting-list group. The waiting-list group received only the current conventional medication, while the EA group received an additional 12 EA sessions on top of the conventional medication. The primary outcome indicators is nerve conduction velocity (NCV), which will be tested at the baseline and week 6. Total clinical efficiency, TCSS, Clinical symptoms score of Traditional Chinese Medicine (TCM), Patient global impression of change(PGIC), Temperature of regions of interest (ROIs), and Physico chemical examination will be used as secondary outcome indicators. In addition, safety assessment will be determined based on adverse events during the trial.
UNASSIGNED: The expected results of this study will determine whether EA improves efficacy in the treatment of DPN with an acceptable safety profile, and investigating variations in the efficacy of EA across different levels of DPN severity. Furthermore, it will explore the viability of IRT as an objective measure for evaluating treatment effectiveness for DPN.
UNASSIGNED: ClinicalTrials.gov identifier, NCT06054087.

Sleep quality (SQ) is a crucial aspect of overall health. Poor sleep quality may cause cognitive impairment, mood disturbances, and an increased risk of chronic diseases. Therefore, assessing sleep quality helps identify individuals at risk and develop effective interventions. SQ has been demonstrated to affect heart rate variability (HRV) and skin temperature even during wakefulness. In this perspective, using wearables and contactless technologies to continuously monitor HR and skin temperature is highly suited for assessing objective SQ. However, studies modeling the relationship linking HRV and skin temperature metrics evaluated during wakefulness to predict SQ are lacking. This study aims to develop machine learning models based on HRV and skin temperature that estimate SQ as assessed by the Pittsburgh Sleep Quality Index (PSQI). HRV was measured with a wearable sensor, and facial skin temperature was measured by infrared thermal imaging. Classification models based on unimodal and multimodal HRV and skin temperature were developed. A Support Vector Machine applied to multimodal HRV and skin temperature delivered the best classification accuracy, 83.4%. This study can pave the way for the employment of wearable and contactless technologies to monitor SQ for ergonomic applications. The proposed method significantly advances the field by achieving a higher classification accuracy than existing state-of-the-art methods. Our multimodal approach leverages the synergistic effects of HRV and skin temperature metrics, thus providing a more comprehensive assessment of SQ. Quantitative performance indicators, such as the 83.4% classification accuracy, underscore the robustness and potential of our method in accurately predicting sleep quality using non-intrusive measurements taken during wakefulness.

The objective of this work was to evaluate the effect of using naturally shaded pastures on scrotal thermoregulatory capacity, testicular echotexture, and sperm morphology of Nelore (Bos indicus) and Canchim (5/8 Bos taurus x 3/8 Bos indicus) bulls in a tropical climate region. Sixty-four adult Nelore and Canchim bulls were used, equally allocated in Full Sun (FS, n = 32) or Crop-Livestock-Forestry (CLF, n = 32) pasture systems. During five consecutive climate seasons, the bulls underwent monthly breeding soundness evaluations and the biometeorological variables in the systems were continuously monitored. Microclimate was significantly different between systems. CLF system had lower BGHI than FS throughout the experimental period. No triple interaction (Season x Breed x Treatment, P > 0.05) was observed for any of the variables. Animals in CLF showed lower body temperature in Summer (FS:39.41 ± 0.05 vs. CLF:39.30 ± 0.05 °C; P = 0.005) and in Autumn (FS:39.54 ± 0.05 vs. CLF:39.35 ± 0.05 °C; P = 0.005). Access to shading did not determine differences in the evolution of scrotal biometry, temperatures, and scrotal thermal gradients (P > 0.05). Regardless of breed, animals in CLF showed greater right testicular volume (FS:247.5 ± 5.7 vs. CLF:259.0 ± 5.7 cm³; P < 0.05), more suitable parenchyma echotexture, and fewer microlithiasis spots in the Spring and Summer. Testosterone concentration was higher in FS (FS:2.6 ± 0.2 vs. CLF:2.1 ± 0.2 ng/mL; P = 0.035). Canchim bulls presented higher total sperm defects during the Autumn and Winter (P = 0.010), but the total defects levels for Canchim and Nelore bulls were in normal range for adult bulls. Thus, the natural shade in CLF system was effective in improving the microclimate of pastures and minimizing adverse environmental effects on some reproductive features of interest in beef cattle.

Infrared thermography (IRT) is a well-known imaging technique that provides a non-invasive displaying of the ocular surface temperature distribution. Currently, compact smartphone-based IRT devices, as well as special software for processing thermal images, have become available. The study aimed to determine the possible use of smartphone-based IRT devices for real-time ocular surface thermal imaging. This study involved 32 healthy individuals (64 eyes); 10 patients (10 eyes) with proliferative diabetic retinopathy (PDR) and absolute glaucoma; 10 patients (10 eyes) with PDR, who underwent vitreoretinal surgery. In all cases, simultaneous ocular surface IRT of both eyes was performed. In healthy individuals, the ocular surface temperature (OST) averaged 34.6 ± 0.8 °C and did not differ substantially between the paired eyes, in different age groups, and after pupil dilation. In our study, high intraocular pressure was accompanied by a decrease in OST in all cases. After vitreoretinal surgery in cases with confirmed subclinical inflammation, the OST was higher than the baseline and differed from that of the paired eye by more than 1.0 °C. These results highlight that smartphone-based IRT imaging could be useful for the non-invasive detection of OST asymmetry between paired eyes due to increased intraocular pressure or subclinical inflammation.

UNASSIGNED: Infrared thermography is gaining attention in the field of sports medicine and performance. This study investigated the effects of static and dynamic warm-ups and a 90° change of direction (COD) exercise on the thermal response of the knee.
UNASSIGNED: Thermograms were collected using the FlIR E54 Imaging Camera from 85 healthy young adults, 46 men and 39 women, aged 20-31 years. The participants were divided in two groups, static and dynamic warm-up. Four thermograms were taken: baseline (T0), warm-up (T1), COD (T2), and rest (T3). Four regions of interest (ROIs) of the knee were analyzed: anterior upper half (AUH), anterior lower half (ALH), posterior upper half (PUH), and posterior lower half (PLH). Mixed ANOVA with the Bonferroni-Holm test and independent t-test were used for pairwise comparison and to spot differences between the right and left knees at T1 and T2 and at T0 between men and women, respectively.
UNASSIGNED: The mixed ANOVA was significant for time points (p< 0.001) in all the ROIs and for the stretching/temperature interaction with different levels of significance. The t-test results for the right and left knees at T1 and T2 were not significant. The temperature in the static warm-up group followed a decrease at T1, a subsequent decrease at T2, and a recovery similar to the baseline at T3, in the ALH in men and women and in the PUH only in men.
UNASSIGNED: Static stretching was more suitable for preparing the knee for the COD exercise than the dynamic one in terms of the thermal response.

This study evaluated the impact of coat color (CC) and hair coat characteristics (HC) on productive and physiological traits related to thermotolerance in Angus heifers. The goal was to determine if HC and/or CC were reliable indicators of thermotolerance on a large scale for future breeding programs. Ninety-three 15-month-old Angus heifers (52 black, 41 red) were evaluated in three periods on a beef cattle farm in Brazil. Heifers were classified by CC and HC, and body weight, body condition score (BCS), and reproductive tract score (RTS) were compared between groups. In the summer evaluation, surface temperature (infrared thermography), internal temperature (intravaginal sensors), sweating rate, and behavior were assessed in a subset of heifers. Temperature-humidity index (THI) was calculated using meteorological data. The proportion of heifers with short, fine, and smooth hair (HC1) increased (P < 0.05) over the evaluations. Heifers with thick, long, and woolly hair (HC3) had lower (P < 0.05) body weights than those with finer coats, regardless of CC. Black heifers had greater (P < 0.05) puberty rates than red heifers in the first two evaluations. At a THI of 66, black heifers with HC1 exhibited a lower (P < 0.05) internal temperature compared to black heifers with HC3. At a THI of 75, all heifers with HC1 had lower (P < 0.05) internal temperatures, regardless of CC. Red heifers and those with HC3 experienced hyperthermia for longer (P < 0.05) periods. Neither HC nor CC affected (P > 0.05) surface temperatures or sweating rates. At a THI of 72, more black heifers remained standing, suggesting behavioral adaptation. In conclusion, coat color and characteristics influence thermal stress and performance in Angus heifers, though color impact is limited. Internal temperature monitoring effectively determines thermotolerance. In tropical regions, selecting for short, fine, smooth hair may improve heat tolerance.

Background  Early detection of free flap compromise is critical for salvage of the flap. Various methods of free flap monitoring have been described, but clinical assessment is the standard method for among all. In this study, role of infrared thermography is evaluated for free flap monitoring. Materials and Methods  In patients undergoing free flap surgery, monitoring was done using standard clinical parameters and infrared thermography as per our institutional protocol. Mean temperature difference (∆T) between the flap and the surrounding skin was calculated using the temperature readings from the thermal images intra- and postoperatively. The accuracy of infrared thermography in flap monitoring was assessed in comparison to the standard clinical protocol. Results  Forty-one flaps were included in the analysis, out of which five flaps got compromised. It was observed that the mean temperature difference was higher (mean ∆T 0.20-0.59 vs. 2.38-3.32) when there was a flap compromise, and this temperature difference was evident even before the development of clinical signs. The temperature difference in venous thrombosis (mean ∆T 1.0-2.7) was found to be slightly lower than in arterial insufficiency (mean ∆T 2.1-4.4). For a ∆T cutoff value of 2°C, the thermal camera had a sensitivity of 88.6%, specificity of 98.9%, positive predictive value of 93.9%, and negative predictive value of 97.7%. Conclusion  Infrared thermography is a valuable and noninvasive objective tool in free flap monitoring, which can detect flap compromise (increasing value of ∆T) even before it becomes clinically evident.

UNASSIGNED: Hypoperfusion, a common manifestation of many critical illnesses, could lead to abnormalities in body surface thermal distribution. However, the interpretation of thermal images is difficult. Our aim was to assess the mortality risk of critically ill patients at risk of hypoperfusion in a prospective cohort by infrared thermography combined with deep learning methods.
UNASSIGNED: This post-hoc study was based on a cohort at high-risk of hypoperfusion. Patients\' legs were selected as the region of interest. Thermal images and conventional hypoperfusion parameters were collected. Six deep learning models were attempted to derive the risk of mortality (range: 0 to 100%) for each patient. The area under the receiver operating characteristic curve (AUROC) was used to evaluate predictive accuracy.
UNASSIGNED: Fifty-five hospital deaths occurred in a cohort consisting of 373 patients. The conventional hypoperfusion (capillary refill time and diastolic blood pressure) and thermal (low temperature area rate and standard deviation) parameters demonstrated similar predictive accuracies for hospital mortality (AUROC 0.73 and 0.77). The deep learning methods, especially the ResNet (18), could further improve the accuracy. The AUROC of ResNet (18) was 0.94 with a sensitivity of 84% and a specificity of 91% when using a cutoff of 36%. ResNet (18) presented a significantly increasing trend in the risk of mortality in patients with normotension (13 [7 to 26]), hypotension (18 [8 to 32]) and shock (28 [14 to 62]).
UNASSIGNED: Interpreting infrared thermography with deep learning enables accurate and non-invasive assessment of the severity of patients at risk of hypoperfusion.