Infectious keratitis is still one of the major causes of visual impairment and blindness, often affecting developing countries. Eye-drop therapy to reduce disease progression is the first line of treatment for infectious keratitis. The current limitations in controlling ophthalmic infections include rapid precorneal drug loss and the inability to provide long-term extraocular drug delivery. The aim of the present study was to develop a novel ophthalmic formulation to treat corneal infection. The formulation was prepared by constructing moxifloxacin (MFX) and dexamethasone (DEX)-loaded nanostructured lipid carriers (Lipo-MFX/DEX) mixed with a collagen/gelatin/alginate (CGA) biodegradable material (CGA-Lipo-MFX/DEX) for prolonged ocular application. The characteristics of the prepared Lipo-MFX/DEX nanoparticles were as follows: average size, 132.1 ± 73.58 nm; zeta potential, −6.27 ± 4.95 mV; entrapment efficiency, 91.5 ± 3.5%; drug content, 18.1 ± 1.7%. Our results indicated that CGA-Lipo-MFX/DEX could release an effective working concentration in 60 min and sustain the drug release for at least 12 h. CGA-Lipo-MFX/DEX did not produce significant toxicities, but it increased cell numbers when co-cultured with ocular epithelial cells. An animal study also confirmed that CGA-Lipo-MFX/DEX could inhibit pathogen microorganism growth and improve corneal wound healing. Our results suggest that CGA-Lipo-MFX/DEX could be a useful anti-inflammatory formulation for ophthalmological disease treatment.

The predictive value of the pretreatment prognostic nutritional index (PNI) for head and neck cancer (HNC) remains controversial. We conducted a meta-analysis to assess the predictive value of PNI in HNC patients. A systematic search through internet databases including PubMed, Embase, and Cochrane Library for qualified studies estimating the association of PNI with HNC patient survival was performed. Overall survival (OS), progression-free survival (PFS), disease-specific survival (DSS), disease-free survival (DFS) and distant metastasis-free survival (DMFS) data were collected and evaluated. A random-effects model was used to calculate the pooled hazard ratios (pHRs) and corresponding 95% confidence intervals (CIs). A total of 7815 HNC patients from 14 eligible studies were involved. Pooled analysis showed that low pretreatment PNI was correlated with poor OS (pHR: 1.93, 95% CI 1.62–2.30, p < 0.001), PFS (pHR: 1.51, 95% CI 1.19–1.92, p = 0.008), DSS (pHR: 1.98, 95% CI 1.12–3.50, p < 0.001), DFS (pHR: 2.20, 95% CI 1.66–2.91, p < 0.001) and DMFS (pHR: 2.04, 95% CI 1.74–2.38, p < 0.001). Furthermore, low pretreatment PNI was correlated with poor OS despite variations in the cancer site, sample size, PNI cut-off value, analysis method (multivariate analysis or univariate analysis) and treatment modality in subgroup analysis. Elevated pretreatment PNI is correlated with a superior prognosis in HNC patients and could be used as a biomarker in clinical practice for prognosis prediction and treatment stratification.

An important challenge of running large-scale cloud services in a geo-distributed cloud system is to minimize the overall operating cost. The operating cost of such a system includes two major components: electricity cost and wide-area-network (WAN) communication cost. While the WAN communication cost is minimized when all virtual machines (VMs) are placed in one datacenter, the high workload at one location requires extra power for cooling facility and results in worse power usage effectiveness (PUE). In this paper, we develop a model to capture the intrinsic trade-off between electricity and WAN communication costs, and formulate the optimal VM placement problem, which is NP-hard due to its binary and quadratic nature. While exhaustive search is not feasible for large-scale scenarios, heuristics which only minimize one of the two cost terms yield less optimized results. We propose a cost-aware two-phase metaheuristic algorithm, Cut-and-Search, that approximates the best trade-off point between the two cost terms. We evaluate Cutand-Search by simulating it over multiple cloud service patterns. The results show that the operating cost has great potential of improvement via optimal VM placement. Cut-and-Search achieves a highly optimized trade-off point within reasonable computation time, and outperforms random placement by 50%, and the partial-optimizing heuristics by 10-20%.

Osteoarthritis (OA) remains one of the common degenerative joint diseases and a major cause of pain and disability in older adult individuals. Oral administration of non-steroidal anti-inflammatory drugs (NSAIDs) (such as diclofenac, DIC) or intra-articular injected gluco-corticosteroids (such as dexamethasone, DEX) were the conventional treatment strategies for OA to reduce joint pain. Current limitations for both drugs including severe adverse effects with risks of toxicity were noted. The aim of the present study was to generate a novel OA treatment formulation hyaluronic acid (HA)-Liposomal (Lipo)-DIC/DEX to combat joint pain. The formulation was prepared by constructing DIC with DEX-loaded nanostructured lipid carriers Lipo-DIC/DEX mixed with hyaluronic acid (HA) for prolonged OA application. The prepared Lipo-DIC/DEX nanoparticles revealed the size as 103.6 ± 0.3 nm on average, zeta potential as −22.3 ± 4.6 mV, the entrapment efficiency of 90.5 ± 5.6%, and the DIC and DEX content was 22.5 ± 4.1 and 2.5 ± 0.6%, respectively. Evidence indicated that HA-Lipo-DIC/DEX could reach the effective working concentration in 4 h and sustained the drug-releasing time for at least 168 h. No significant toxicities but increased cell numbers were observed when HA-Lipo-DIC/DEX co-cultured with articular chondrocytes cells. Using live-animal In vivo imaging system (IVIS), intra-articular injection of each HA-Lipo-DIC/DEX sufficed to reduce knee joint inflammation in OA mice over a time span of four weeks. Single-dose injection could reduce the inflammation volume down to 77.5 ± 5.1% from initial over that time span. Our results provided the novel drug-releasing formulation with safety and efficiency which could be a promising system for osteoarthritis pain control.