A particular phenomenon was found in packed beds with dielectric barrier discharge (DBD): The temperature of the gaseous phase is significantly higher than that of the pellet phase. This is completely different from the conventional packed bed where the phases have essentially the same temperature. A simplified model was developed to estimate this phenomenon in the packed bed with DBD under natural convection. The power consumption mechanism was investigated. The mechanisms for the electrical energy of inlet plasma transformed into heat energy in the two phases, gaseous and pellets of the packed bed reactor, are different. The energy transformation in the gaseous phase includes electron absorption as well as ion absorption. The energy absorbed by electrons, including ohmic heating and stochastic heating, maintains plasma condition. The energy absorbed by ions transfers to other heavy particles with frequent collisions in plasma sheath and, finally, raises the macro temperature of the gas. The energy consumption in pellet phase is dielectric polarization loss and depends on packed bed geometry and DBD. These mechanisms of energy consumption are introduced into the packed bed heat transfer model to develop the simplified model. Some experiments are performed and the results agree well with the numerical simulation of this model.

Objective: With the characteristics of population change, geriatric hip fracture is increasing, accompanied by high morbidity and mortality rates. However, limited research has thoroughly investigated the postsurgery functional outcomes of hip fractures in the elderly population.Methods: This study included 993 patients who underwent hip fracture surgery, drawn from a prospective cohort in China. Demographic and clinical data were collected for all participants. The cohort was randomly divided into training and validation sets (8:2). Least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses were employed to identify predictive factors for hip function at 12 months postoperatively. A nomogram was developed using R software and evaluated using concordance index (C‐indexes), area under the curve (AUC), decision curve analysis (DCA), and calibration curves.Results: Patients were divided into training (n = 794) and validation set (n = 199). Eight independent predictive variables for the poor functional outcome (Harris Hip Score < 80) after hip fracture include age (odds ratio [OR], 1.08; 95% confidence interval [CI], 1.04–1.12), hypertension history (OR, 2.53; 95% CI, 1.50–4.23), fracture type (OR, 0.28; 95% CI, 0.17–0.48), blood transfusion (OR, 2.30; 95% CI, 1.35–3.94), baseline PARKER score (OR, 0.85; 95% CI, 0.75–0.97), adverse events occurred within 12 months postoperatively (OR, 5.49; 95% CI, 2.30–13.08), transfer to the rehabilitation institution (OR, 3.22; 95% CI, 1.51–6.88), and time from surgery to weight‐bearing (OR, 1.02; 95% CI, 1.01–1.03). The nomogram demonstrated excellent predictive ability in the training set (AUC = 0.853, [95% CI: 0.816–0.890]). Furthermore, according to the calibration curve, the model's prediction and actual observation were in good consistency, and the DCA curve demonstrated good clinical usefulness.Conclusions: This study developed a personalized, predictive nomogram with eight risk factors for predicting 1‐year functional outcomes in geriatric patients with hip fractures. Our model facilitates the early identification of high‐risk patients and enables surgeons to implement timely preventive interventions.

Background: Osteoarthritis (OA) is a chronic joint disease marked by the progressive loss of articular cartilage and associated inflammatory processes, which can lead to significant pain and disability. However, the heterogeneous nature of OA has limited the effectiveness of many anti-inflammatory interventions, highlighting the need for phenotype-driven therapeutic strategies. Methods: This narrative review was conducted by searching PubMed, Web of Science, and Google Scholar from inception to June 2025. Predefined keywords included "osteoarthritis”, "inflammation”, "pain”, "synovitis”, "effusion”, "treatment”, "therapy” and "clinical trials”. Additional relevant publications were identified through citation tracking of the included papers. Results: Our analysis of recent randomized clinical trials (RCTs) reveals a clear divergence in therapeutic efficacy, while targeted biologics (eg, TNF-α inhibitors like adalimumab and IL-1 blockers such as lutikizumab) have largely failed to meet primary pain endpoints in unselected populations, broad-spectrum immunomodulators like methotrexate (MTX) show significant promise, particularly in reducing pain and stiffness for the inflammatory phenotype. Furthermore, emerging evidence suggests that intra-articular (IA) treatments and phenotype-driven stratifications are essential to overcoming the current "one-size-fits-all” failure in OA drug development. Conclusion: The central message of this review is that the inconsistent efficacy of anti-inflammatory therapies in OA reflects a patient-selection problem more than a failed target: agents tested in unselected populations, including most anti-cytokine biologics, have not relieved pain, whereas signals emerge for MTX in patients with an inflammatory phenotype. Stratifying patients by imaging-defined synovitis or effusion and inflammatory biomarkers is therefore the key unmet need, and phenotype-driven trial design—rather than new molecular targets alone—offers the most promising path to effective inflammation-targeted treatment of OA pain.