To report the corneal stroma cell density evolution identified by in vivo corneal confocal microscopy in humans using injected autologous adipose-derived adult stem cells (ADASCs) and corneal decellularized laminas in corneas with advanced keratoconus. Interventional prospective, consecutive, randomized, comparative series of cases. A total of 14 keratoconic patients were randomly distributed into three groups for three types of surgical interventions: group 1 (G-1), autologous ADASC implantation (n = 5); group 2 (G-2), decellularized human corneal stroma (n = 5); and group 3 (G-3), autologous ADASCs + decellularized human corneal stroma (n = 4). A gradual and significant increase (P < 0.001) was observed in the cellularity in the anterior and posterior stroma of patients in G-1, G-2, and G-3 a year after the surgery in comparison with the preoperative density level. The same result was observed at the mid-corneal stroma in G-1 and at the anterior and posterior surfaces and within the laminas in G-2 and G-3. The cell density of patients receiving ADASC recellularized laminas (G-3) was statistically significantly higher (P = 0.011) at the anterior surface and within the lamina (P = 0.029) and at the posterior surface than in those implanted only with decellularized laminas (G-2). A significant increase in cell density occurred up to 1 postoperative year at the corneal stroma following the implantation of ADASCs alone, as well as in those cases implanted with decellularized and recellularized laminas at the different levels of the analysis. However, this increase was significantly higher in the ADASC recellularized laminas.

The most described techniques used to detect diabetic retinopathy and diabetic macular edema have to be interpreted correctly, such that a person not specialized in ophthalmology, as is usually the case of a primary care physician, may experience difficulties with their interpretation; therefore we constructed, validated and implemented as a mobile app a new tool to detect diabetic retinopathy or diabetic macular edema (DRDME) using simple objective variables. We undertook a cross-sectional, observational study of a sample of 142 eyes from Spanish diabetic patients suspected of having DRDME in 2012–2013. Our outcome was DRDME and the secondary variables were: type of diabetes, gender, age, glycated hemoglobin (HbA1c), foveal thickness and visual acuity (best corrected). The sample was divided into two parts: 80% to construct the tool and 20% to validate it. A binary logistic regression model was used to predict DRDME. The resulting model was transformed into a scoring system. The area under the ROC curve (AUC) was calculated and risk groups established. The tool was validated by calculating the AUC and comparing expected events with observed events. The construction sample (n= 106) had 35 DRDME (95% CI [24.1–42.0]), and the validation sample (n= 36) had 12 DRDME (95% CI [17.9–48.7]). Factors associated with DRDME were: HbA1c (per 1%) (OR = 1.36, 95% CI [0.93–1.98],p= 0.113), foveal thickness (per 1 µm) (OR = 1.03, 95% CI [1.01–1.04],p< 0.001) and visual acuity (per unit) (OR = 0.14, 95% CI [0.00–0.16],p< 0.001). AUC for the validation: 0.90 (95% CI [0.75–1.00],p< 0.001). No significant differences were found between the expected and the observed outcomes (p= 0.422). In conclusion, we constructed and validated a simple rapid tool to determine whether a diabetic patient suspected of having DRDME really has it. This tool has been implemented on a mobile app. Further validation studies are required in the general diabetic population.

Clinical inertia has been defined as mistakes by the physician in starting or intensifying treatment when indicated. Inertia, therefore, can affect other stages in the healthcare process, like diagnosis. The diagnosis of dyslipidemia requires ≥2 high lipid values, but inappropriate behavior in the diagnosis of dyslipidemia has only previously been analyzed using just total cholesterol (TC). To determine clinical inertia in the dyslipidemia diagnosis using both TC and high-density lipoprotein cholesterol (HDL-c) and its associated factors. Cross-sectional. All health center visits in the second half of 2010 in the Valencian Community (Spain). 11,386 nondyslipidemic individuals aged ≥20 years with ≥2 lipid determinations. Gender, atrial fibrillation, hypertension, diabetes, cardiovascular disease, age, and ESCARVAL training course. Lipid groups: normal (TC<5.17 mmol/L and normal HDL-c [≥1.03 mmol/L in men and ≥1.29 mmol/L in women], TC inertia (TC≥5.17 mmol/L and normal HDL-c), HDL-c inertia (TC<5.17 mmol/L and low HDL-c), and combined inertia (TC≥5.17 mmol/L and low HDL-c). TC inertia: 38.0% (95% CI: 37.2–38.9%); HDL-c inertia: 17.7% (95% CI: 17.0–18.4%); and combined inertia: 9.6% (95% CI: 9.1–10.2%). The profile associated with TC inertia was: female, no cardiovascular risk factors, no cardiovascular disease, middle or advanced age; for HDL-c inertia: female, cardiovascular risk factors and cardiovascular disease; and for combined inertia: female, hypertension and middle age. Cross-sectional study, under-reporting, no analysis of some cardiovascular risk factors or other lipid parameters. A more proactive attitude should be adopted, focusing on the full diagnosis of dyslipidemia in clinical practice. Special emphasis should be placed on patients with low HDL-c levels and an increased cardiovascular risk.