Search Results (35)

Background/Objectives: The aim of the study is to first evaluate mid- to long-term changes in shoulder range of motion (ROM) and functional performance in activities of daily living (ADLs) after arthroscopic Bankart repair using three-dimensional (3D) motion analysis. Methods: We prospectively analyzed five patients (mean age: 31.8 years) pre- and postoperatively at 8.4 months and eight patients retrospectively at 12.1 years (mean age: 40.4 years). Fifteen asymptomatic adults served as controls. Shoulder kinematics were assessed using the Heidelberg Upper Extremity (HUX) model during maximum ROM and four ADL tasks (apron, neck, wash, and book). Results: At short-term follow-up, forward flexion improved by 31° (p < 0.05) and abduction improved by 70° (p < 0.05), while other movements showed non-significant trends toward improvement. Long-term follow-up demonstrated sustained or increased gains in flexion (+9°) and abduction (+7°) but significant declines in external rotation (−5°) and internal rotation (−30°). ADL analyses showed significant postoperative gains in abduction/adduction during “apron” (+6.7°) and “neck” (+49.8°) tasks. The long-term results remained comparable to or better than postoperative values in most planes, although external/internal rotation during the “wash” task decreased over time. Compared with normative controls, patients employed a larger ROM during some ADLs, suggesting compensatory mechanisms. Conclusions: Arthroscopic Bankart repair yields sustained mid- to long-term improvements in shoulder ROM and ADL performance. Rotational deficits persist despite maintained flexion and abduction in the long run, underscoring the need for targeted rehabilitation strategies. Full article

Background: To report a deep learning-based algorithm for automated segmentation of geographic atrophy (GA) among patients with age-related macular degeneration (AMD). Methods: Validation of a deep learning algorithm was performed using optical coherence tomography (OCT) images from patients in routine clinical care diagnosed with GA, with and without concurrent nAMD. For model construction, a 3D U-Net architecture was used with the output modified to generate a 2D mask. Accuracy of the model was assessed relative to the manual labeling of GA with the Dice similarity coefficient (DSC) and correlation r² scores. Results: The OCT data set included 367 scans from the Spectralis (Heidelberg, Germany) from 55 eyes in 33 subjects; 267 (73%) scans had concurrent nAMD. In parallel, 348 scans were collected using the Cirrus (Zeiss), from 348 eyes in 326 subjects; 101 (29%) scans had concurrent nAMD. For Spectralis data, the mean DSC score was 0.83 and r² was 0.91. For Cirrus data, the mean DSC score was 0.82 and r² was 0.88. Conclusions: The reported deep learning algorithm demonstrated strong agreement with manual grading of GA secondary to AMD on the OCT data set from routine clinical practice. The model performed well across two OCT devices as well as amongst patients with GA with concurrent nAMD, suggesting applicability in the clinical space. Full article

Background/Objectives: The COVID-19 pandemic and the development of vaccines provided the opportunity to monitor disease prevalence and outcomes, vaccinations, their side effects and serological responses in patients with inflammatory bowel disease (IBD). Methods: IBD patients of the outpatient clinic at the University Hospital Heidelberg who completed at least one questionnaire on COVID-19 and related vaccinations from July 2021 to August 2022 were included. Spike-IgG antibody titres were determined. Friedman tests, Wilcoxon signed-rank tests and Kruskal–Wallis tests were used for comparisons. The influence of IBD therapy was analysed using linear models with mixed effects. Results: The cohort included 520 patients (269 females, mean age = 45.3 years, 60.6% with Crohn’s disease, 35.4% with ulcerative colitis, and 4.0% with unclassified IBD). Four hundred eighty patients (92.3%) received at least one COVID-19 vaccination, and 154 patients (29.6%) were infected by SARS-CoV-2. Among all of them, 94.4% achieved seroconversion. Triple-vaccinated patients with additional SARS-CoV-2 infection developed the highest serological responses (χ² = 16.51, p < 0.001, df = 3). An antibody decay over time was observed after the second (p < 0.001) and third vaccinations (p < 0.001). Regarding individual IBD medications, no differences in mean titres were found after two (χ² = 6.60, p = 0.36, df = 6) versus three vaccinations (χ² = 4.97, p = 0.42, df = 5). Linear models with mixed effects revealed no influence of IBD therapies on serological responses. Conclusions: The highest serological responses were observed in IBD patients after three vaccinations plus SARS-CoV-2 infection without significant differences between IBD therapies. Full article

(1) Background: The reliability of port equipment is of significant interest to industry stakeholders due to the economic and logistical factors governing the operation of maritime container terminals. Failures of key equipment like quay cranes can halt operations or cause economically significant delays. (2) Methods: The impact assessment of these disruptive events is conducted through terminal activity modeling and discrete-event simulation of internal processes. The system’s steady-state or transient condition, induced by disruptive events, is statistically assessed within a set of scenarios proposed by the authors. (3) Results: The Heidelberg–Welch and Geweke tests enabled the evaluation of steady-state and transient conditions within the modeled system, which was affected by the reduced reliability of container-handling equipment. (4) Conclusions: The research findings confirmed the usefulness of modeling and simulation in assessing the impact of equipment reliability on maritime container terminal operations. If the magnitude of the disruptive event exceeds the terminal’s absorption capacity, the system may become blocked or remain in a transient state without the ability to recover. This underscores the necessity of analyzing the reliability of critical handling equipment and implementing corrective maintenance actions when required. Full article

Background: Despite the significant advancements of liver surgery in the last few decades, the survival rate of patients with liver and pancreatic cancers has improved by only 10% in 30 years. Precision medicine offers a patient-centered approach, which, when combined with machine learning, could enhance decision making and treatment outcomes in surgical management of ihCC. This study aims to develop a decision support model to optimize treatment strategies for patients with ihCC, a prevalent primary liver cancer. Methods: The decision support model, named MedMax, was developed using three data sources: studies retrieved through a systematic literature review, expert opinions from HPB surgeons, and data from ihCC patients treated at Heidelberg University Hospital. Expert opinions were collected via surveys, with factors rated on a Likert scale, while patient data were used to validate the model’s accuracy. Results: The model is structured into four decision-making phases, assessing diagnosis, treatment modality, surgical approach, and prognosis. Prospectively, 44 patients with ihCC were included for internal primary validation of the model. MedMax could predict the appropriate treatment considering the resectability of the lesions in 100% of patients. Also, MedMax could predict a decent surgical approach in 77% of the patients. The model proved effective in making decisions regarding surgery and patient management, demonstrating its potential as a clinical decision support tool. Conclusions: MedMax offers a transparent, personalized approach to decision making in HPB surgery, particularly for ihCC patients. Initial results show high accuracy in treatment selection, and the model’s flexibility allows for future expansion to other liver tumors and HPB surgeries. Further validation with larger patient cohorts is required to enhance its clinical utility. Full article

Increasing evidence suggests that dalbavancin is an effective long-term treatment for ventricular assist device (VAD) infections, with various prolonged dosing regimens currently in use. This retrospective study aimed to assess dalbavancin pharmacokinetics in VAD patients and identify optimal, feasible dosing regimens for long-term suppressive outpatient therapy. Data from Heidelberg University Hospital’s VAD register were analyzed using non-linear mixed-effects modeling for pharmacokinetic analysis and dosing simulations (Lixoft^®). The probability of target attainment (PTA) and cumulative fraction of response (CFR) were calculated for different protein-binding scenarios considering the minimum inhibitory concentration (MIC) distribution of Staphylococcus aureus. Using data from 13 patients with 38 blood samples, a two-compartment model best described the dalbavancin pharmacokinetics, with a typical value for clearance of 0.050 L/h, central volume of distribution of 6.5 L, and peripheral volume of 15.4 L. No covariates significantly improved the model fit. The observed protein binding varied between 96 and 98%. Dosing simulations demonstrated that 1500 mg every 3 weeks ensured the target attainment for stasis at MIC values of 0.125 mg/L (PTA ≥ 90%) up to a protein binding of 99%. Considering the CRF, longer dosing intervals up to 5 weeks might be possible. Depending on individual MICs and protein binding, a dalbavancin regimen of 1500 mg every 3 to 5 weeks therefore appears to be a valuable option for outpatient therapy of VAD infections. Therapeutic drug monitoring should be considered to manage inter-individual variability and to support clinicians in long-term treatments of subacute and chronic infections. Full article

Background and Objectives: The experience of unpleasant sensory phenomena after lower limb amputations (LLAs), including phantom limb pain (PLP), phantom limb sensation (PLS), and residual limb pain (RLP), impacts global healthcare and adversely affects outcomes post-amputation. This study aimed to describe the distribution of PLP, PLS, and RLP among patients with LLAs registered in the Heidelberg Amputation Registry. The primary objective was to determine the prevalence of sensory abnormalities across different amputation levels and causes. Materials and Methods: In this single-center, cross-sectional study, data from 459 patients registered in the Heidelberg Amputation Registry were analyzed for the occurrence of PLP, PLS and RLP. Subsequently, logistic regression models were used to identify the independent risk factors associated with sensory disturbances following LLAs. The mean age of the LLA patients (31% female, 69% male) was 58 years (SD ± 18). Results: The patients were, on average, 44 years old (SD ± 22) at the time of amputation, with a mean duration since amputation of 15 years (SD ± 17). Transtibial amputations were the most common (43%), followed by transfemoral (39%) and partial foot amputations (10%). Hip and knee disarticulations were observed in 3.7% and 3.5% of the cohort, respectively, with hemipelvectomies accounting for 1%. Traumatic injuries (32%) and neoplastic disorders (22%) were the leading causes of LLAs, while peripheral artery disease and diabetes were responsible for 12% and 6% of cases, respectively. Importantly, a significant proportion of participants (85%) reported experiencing abnormal sensations. The prevalence rates for phantom limb pain (PLP), phantom limb sensation (PLS), and residual limb pain (RLP) were 58%, 66%, and 46%, respectively. The occurrence of sensory disturbances, with the exception of RLP, was significantly affected by the level of amputation. Notably, the age at amputation emerged as an independent risk factor for developing abnormal sensations, including PLS. Conclusions: In conclusion, this study provides a comprehensive overview of sensory abnormalities in a diverse cohort of LLA patients, highlighting the age at amputation as an important factor. The findings emphasize the role of comprehensive registries in enhancing care for individuals with amputations and guiding targeted pain management strategies. Full article

Background: Parkinson’s Disease (PD) is a progressive neurodegenerative condition associated with deficit in reaction time which can lead to falls, resulting in limited independence, diminished quality of life, heightened rates of institutionalization and increased healthcare costs. We aimed to examine the effects of an acupuncture protocol in motor time response after an auditory stimulus. Methods: This study employed a case series design. Reaction time to exposed rhythmic and random auditory stimuli outcomes were evaluated at six different moments over a month-long acupuncture treatment protocol using the MP 36 system from Biopac Systems. Results: We observed a tendency to have more pronounced improvements in the time response in the more affected side of the body compared with the contralateral one. Patients tended to show better values of response to random auditory stimuli compared to rhythmic auditory ones. We also observed a tendency to obtain better results when considering the accumulative effects of the acupuncture protocol. Conclusions: Our findings indicated a possible role of reaction time as a sensitive and useful tool for motor function assessment in PD patients. Also, from our results, we concluded that the acupuncture protocol used may lead to an improvement in efficacy of motor response after aleatory and rhythmic stimulus; we also found a tendency for a higher efficacy of acupuncture in random stimuli responses in the first stages of the disease. However, further in-depth research, including a statistical evaluation with a larger participant pool, is necessary to validate and confirm these promising results. Full article

Rising antimicrobial resistance (AMR) in Salmonella serotypes host-adapted to cattle is of increasing concern to the beef and dairy industry. The bulk of the existing literature focuses on AMR post-slaughter. In comparison, the understanding of AMR in Salmonella among pre-harvest cattle is still limited, particularly in Texas, which ranks top five in beef and dairy exports in the United States; inherently, the health of Texas cattle has nationwide implications for the health of the United States beef and dairy industry. In this study, long-read whole genome sequencing and bioinformatic methods were utilized to analyze antimicrobial resistance genes (ARGs) in 98 isolates from beef and dairy cattle in the Texas Panhandle. Fisher exact tests and elastic net models accounting for population structure were used to infer associations between genomic ARG profiles and antimicrobial phenotypic profiles and metadata. Gene mapping was also performed to assess the role of mobile genetic elements in harboring ARGs. Antimicrobial resistance genes were found to be statistically different between the type of cattle operation and Salmonella serotypes. Beef operations were statistically significantly associated with more ARGs compared to dairy operations. Salmonella Heidelberg, followed by Salmonella Dublin isolates, were associated with the most ARGs. Additionally, specific classes of ARGs were only present within mobile genetic elements. Full article

Background: The aim of this study was to establish a deep learning prediction model for neoadjuvant FLOT chemotherapy response. The neural network utilized clinical data and visual information from whole-slide images (WSIs) of therapy-naïve gastroesophageal cancer biopsies. Methods: This study included 78 patients from the University Hospital of Cologne and 59 patients from the University Hospital of Heidelberg used as external validation. Results: After surgical resection, 33 patients from Cologne (42.3%) were ypN0 and 45 patients (57.7%) were ypN+, while 23 patients from Heidelberg (39.0%) were ypN0 and 36 patients (61.0%) were ypN+ (p = 0.695). The neural network had an accuracy of 92.1% to predict lymph node metastasis and the area under the curve (AUC) was 0.726. A total of 43 patients from Cologne (55.1%) had less than 50% residual vital tumor (RVT) compared to 34 patients from Heidelberg (57.6%, p = 0.955). The model was able to predict tumor regression with an error of ±14.1% and an AUC of 0.648. Conclusions: This study demonstrates that visual features extracted by deep learning from therapy-naïve biopsies of gastroesophageal adenocarcinomas correlate with positive lymph nodes and tumor regression. The results will be confirmed in prospective studies to achieve early allocation of patients to the most promising treatment. Full article

Artificial intelligence (AI) models have received considerable attention in recent years for their ability to identify optical coherence tomography (OCT) biomarkers with clinical diagnostic potential and predict disease progression. This study aims to externally validate a deep learning (DL) algorithm by comparing its segmentation of retinal layers and fluid with a gold-standard method for manually adjusting the automatic segmentation of the Heidelberg Spectralis HRA + OCT software Version 6.16.8.0. A total of sixty OCT images of healthy subjects and patients with intermediate and exudative age-related macular degeneration (AMD) were included. A quantitative analysis of the retinal thickness and fluid area was performed, and the discrepancy between these methods was investigated. The results showed a moderate-to-strong correlation between the metrics extracted by both software types, in all the groups, and an overall near-perfect area overlap was observed, except for in the inner segment ellipsoid (ISE) layer. The DL system detected a significant difference in the outer retinal thickness across disease stages and accurately identified fluid in exudative cases. In more diseased eyes, there was significantly more disagreement between these methods. This DL system appears to be a reliable method for accessing important OCT biomarkers in AMD. However, further accuracy testing should be conducted to confirm its validity in real-world settings to ultimately aid ophthalmologists in OCT imaging management and guide timely treatment approaches. Full article

Background: Pancreatic cancer is one of the most aggressive and lethal cancers. New treatment strategies are highly warranted. Particle radiotherapy could offer a way to overcome the radioresistant nature of pancreatic cancer because of its biological and physical characteristics. Within particles, helium ions represent an attractive therapy option to achieve the highest possible conformity while at the same time protecting the surrounding normal tissue. The aim of this study was to evaluate the cytotoxic efficacy of helium ion irradiation in pancreatic cancer in vitro. Methods: Human pancreatic cancer cell lines AsPC-1, BxPC-3 and Panc-1 were irradiated with photons and helium ions at various doses and treated with gemcitabine. Photon irradiation was performed with a biological cabin X-ray irradiator, and helium ion irradiation was performed with a spread-out Bragg peak using the raster scanning technique at the Heidelberg Ion Beam Therapy Center (HIT). The cytotoxic effect on pancreatic cancer cells was measured with clonogenic survival. The survival curves were compared to the predicted curves that were calculated via the modified microdosimetric kinetic model (mMKM). Results: The experimental relative biological effectiveness (RBE) of helium ion irradiation ranged from 1.0 to 1.7. The predicted survival curves obtained via mMKM calculations matched the experimental survival curves. Mainly additive cytotoxic effects were observed for the cell lines AsPC-1, BxPC-3 and Panc-1. Conclusion: Our results demonstrate the cytotoxic efficacy of helium ion radiotherapy in pancreatic cancer in vitro as well as the capability of mMKM calculation and its value for biological plan optimization in helium ion therapy for pancreatic cancer. A combined treatment of helium irradiation and chemotherapy with gemcitabine leads to mainly additive cytotoxic effects in pancreatic cancer cell lines. The data generated in this study may serve as the radiobiological basis for future experimental and clinical works using helium ion radiotherapy in pancreatic cancer treatment. Full article

Background: The current study aims to evaluate the occurrence of temporal lobe reactions and identify possible risk factors for patients who underwent particle therapy of the skull base. Methods: 244 patients treated for skull base chordoma (n = 144) or chondrosarcoma (n = 100) at the Heidelberg Ion Beam Therapy Center (HIT) using a raster scan technique, were analyzed. Follow-up MRI-scans were matched with the initial planning images. Radiogenic reactions were contoured and analyzed based on volume and dose of treatment. Results: 51 patients with chordoma (35.4%) and 30 patients (30%) with chondrosarcoma experienced at least one temporal lobe reaction within the follow-up period (median 49 months for chondrosarcoma, 62 months for chordoma). Age, irradiated volume, and dose values were significant risk factors for the development of temporal lobe reactions with the highest significance for the value of DMax-7 being defined as the dose maximum in the temporal lobe minus the 7cc with the highest dose (p = 0.000000000019; OR 1.087). Conclusion: Temporal lobe reactions are a common side effect after particle therapy of the skull base. We were able to develop a multivariate model, which predicted radiation reactions with a specificity of 99% and a sensitivity of 52.2%. Full article

Optical coherence tomography (OCT)-based retinal imagery is often utilized to determine influential factors in patient progression and treatment, for which the retinal layers of the human eye are investigated to assess a patient’s health status and eyesight. In this contribution, we propose a machine learning (ML)-based multistage system of stacked multiscale encoders and decoders for the image segmentation of OCT imagery of the retinal layers to enable the following evaluation regarding the physiological and pathological states. Our proposed system’s results highlight its benefits compared to currently investigated approaches by combining commonly deployed methods from deep learning (DL) while utilizing deep neural networks (DNN). We conclude that by stacking multiple multiscale encoders and decoders, improved scores for the image segmentation task can be achieved. Our retinal-layer-based segmentation results in a final segmentation performance of up to $82.25\pm 0.74$% for the Sørensen–Dice coefficient, outperforming the current best single-stage model by $1.55$% with a score of $80.70\pm 0.20$%, given the evaluated peripapillary OCT data set. Additionally, we provide results on the data sets Duke SD-OCT, Heidelberg, and UMN to illustrate our model’s performance on especially noisy data sets. Full article

While prior prospective iso-caloric substitution studies show a robust association between higher intake of animal protein and risk of mortality, associations observed for mortality risk in relation to major food sources of animal protein have been generally more diverse. We used the EPIC-Heidelberg cohort to examine if confounding, notably, by smoking, adiposity, or alcohol intake, could cause inconsistencies in estimated mortality hazard ratios (HR) related to intake levels of different types of meat and dairy products. Higher intakes of red or processed meats, and lower intakes of milk or cheese, were observed among current heavy smokers, participants with obesity, or heavy alcohol drinkers. Adjusting for age, sex, and total energy intake, risk models showed increased all-cause, cardiovascular, and cancer-related mortality with higher red or processed meat intakes (HR ranging from 1.25 [95% confidence interval = 1.15–1.36] to 1.76 [1.46–2.12] comparing highest to lowest tertiles), but reduced risks for poultry, milk, or cheese (HR ranging from 0.55 [0.43–0.72] to 0.88 [0.81–0.95]). Adjusting further for smoking history, adiposity indices, alcohol consumption, and physical activity levels, the statistical significance of all these observed was erased, except for the association of processed meat intake with cardiovascular mortality (HR = 1.36 [CI = 1.13–1.64]) and cheese intake with cancer mortality (HR = 0.86 [0.76–0.98]), which, however, were substantially attenuated. These findings suggest heavy confounding and provide little support for the hypothesis that animal protein, as a nutrient, is a major determinant of mortality risk. Full article