The rapid spread of acute respiratory infections (ARIs) poses a major challenge to global public health, yet current surveillance systems relying on sentinel hospitals and laboratory testing often lack timeliness and precision. In this study, we present an integrated framework combining wastewater-based epidemiology (WBE) with machine learning to establish a fever prevalence-based early warning model for ARIs. Using wastewater data on paracetamol and ibuprofen consumption (n = 603) from Dalian, Beijing, Guangzhou, and other cities, we evaluated statistical, unsupervised, and supervised learning approaches (11 models) for anomaly detection. Based on model validation and evaluation, the interquartile range, isolation forest, neural networks, and logistic regression models demonstrated favorable performance. However, only the warning thresholds derived from the interquartile range method (3.3%) and isolation forest (3.5%) indicated that November 2021 was a non-epidemic period and November 2024 was an epidemic period, which is consistent with the influenza-like illness surveillance results from sentinel hospitals. Our findings demonstrate that WBE, when coupled with machine learning, can provide timely and precise community-level monitoring of fever prevalence, while also offering early-warning capability for fever-dominant emerging respiratory infections and previously unidentified etiological agents such as "Disease X". This approach effectively addresses the limitations of conventional surveillance, such as incomplete coverage and delayed response, and provides a scalable, evidence-based tool to strengthen public health preparedness and response.

Spinel-based transition metal oxides are potential activators for peroxymonosulfate-based advanced oxidation processes (PMS-AOPs) in water purification, yet their sluggish redox cycling, stemming from poor conductivity, remains a critical limitation. In this work, a distiller's grain-derived porous biochar (BC)-decorated MnCo spinel nanosheet catalyst (MnCoOx@BC) is developed and applied for PMS activation. The catalyst exhibits high efficacy in degrading ibuprofen (IBU), achieving complete removal within 5 min with an apparent reaction rate constant of 1.08 min-1 ([IBU] = 10 mg L-1, [catalyst] = 200 mg L-1, [PMS] = 200 mg L-1), which is 18 and 15 times for pristine BC and unsupported MnCoOx, respectively. Characterization and DFT calculation results indicate that the introduction of biochar could improve the conductivity and induce the formation of abundant oxygen defects, which accelerate the redox cycles of Co (III)/Co (II) and Mn (IV)/Mn (III) catalytic centers. This interfacial configuration facilitates the conversion of PMS into peroxysulfate radical (SO5•-) and superoxide radical (O2•-) species, and finally towards singlet oxygen (1O2), establishing a non-radical dominant pathway where 1O2 accounts for over 95% of the reactive species contribution. This work presents an effective strategy for developing defect-controlled catalytic interfaces to modulate the d-band center of metal sites and steer the interfacial electron transfer pathway, offering profound insights into non-radical-dominated mechanisms for the degradation of emerging micropollutants.

Cross-contamination in pharmaceutical industry is a critical threat to patient safety, potentially reducing therapeutic efficacy and causing adverse effects and addiction. Drugs with narrow therapeutic index and high abuse potential are high-risk candidates, such as tizanidine (TZN). Its carryover may trigger harmful effects, withdrawal symptoms, and unintended dependence. Although cleaning validation minimizes these risks, rapid and reliable tools are still needed to support routine monitoring. To address this need, a portable at-line analytical platform was developed for TZN determination in combination with aceclofenac (ACF), ibuprofen (IBF), and paracetamol (PRC). It employs FDA-recommended swabbing approach, followed by smartphone-based TLC coupled with Color Analyzer and ImageJ for image processing. It provides a rapid, simple, and eco-friendly on-site tool for cleaning validation, enabling both quantitative analysis and qualitative screening via color density charts; a combination not achieved by reported methods. The mobile phase was optimized as toluene-acetone-ethanol (2.3:2.7:0.6 v/v/v), and smartphone-based analysis parameters were optimized. Validation according to ICH guidelines demonstrated good linearity and high sensitivity for the investigated drugs, with quantitation limits below established acceptance limits. Multidimensional sustainability was confirmed using moGAPI, CACI, VIGI, and RGB-12 assessments, highlighting the method as a novel sustainable tool for reliable cleaning validation of TZN combinations.

We report the case of a 23-year-old woman with a pre-existing penicillin allergy label from childhood who presented with severe bilateral conjunctivitis and oral mucositis following a prodromal respiratory illness. Her symptoms worsened shortly after receiving doxycycline, raising concern for a drug reaction. Her condition progressed with significant ocular involvement requiring bilateral amniotic membrane grafting and subsequent immunosuppressive therapy. Investigations confirmed Mycoplasma pneumoniae infection, supporting a diagnosis most consistent with Mycoplasma-induced rash and mucositis (MIRM), although Stevens-Johnson syndrome (SJS) could not be fully excluded. Following recovery, a structured allergy work-up was undertaken, including patch testing, intradermal testing and graded oral provocation in accordance with established guidance. All tests were negative, and the patient successfully tolerated amoxicillin, doxycycline and ibuprofen without adverse reactions. This case demonstrates how a structured, multidisciplinary approach to drug allergy evaluation can facilitate de-labelling in patients with complex mucocutaneous presentations, restore access to first-line therapies and improve patient care.

Nonsteroidal anti-inflammatory drugs (NSAIDs), such as ibuprofen, naproxen, and indomethacin, have reduced amyloid beta (Aβ) levels and improved cognitive function in mouse models of Alzheimer's disease (AD). The objective of the current research is to evaluate different NSAIDs using computer-aided drug design (CADD) to explore their potential as new treatments for AD. CADD is an advanced technique for designing biologically active molecules and involves two primary approaches: structure-based virtual screening and ligand-based virtual screening. After identifying the NSAID with the highest affinity for amyloid Aβ1-42 fibrils (PDB 2BEG), we designed analogs through bioisosteric modifications. Twenty derivatives were created, and two carprofen analogs with the most promising properties were synthesized, yielding 75% success with compound A-1 and 70% with compound A-2. These analogs exhibited lower predicted IC50 values (1.57 μM) and more negative interaction energies (-6.43 and -6.67 kcal/mol for A-1 and A-2, respectively) compared to carprofen. Safety evaluations in L929 cells indicated that these compounds were safe at concentrations of 40 µM. In conclusion, two carprofen analogs were successfully designed and synthesized, demonstrating higher affinity for the amyloid Aβ1-42 fibrils, better-predicted IC50 values than carprofen, and safety in the L929 in vitro assay.

The presence of pharmaceuticals in treated wastewater has become an environmental concern due to their persistence, biological activity, and incomplete removal in conventional wastewater treatment systems. In this study, a g-C3N4/rGO nanocomposite was synthesized via thermal polycondensation of melamine in the presence of reduced graphene oxide and evaluated as a photocatalyst for the degradation of selected pharmaceuticals in membrane bioreactor (MBR)-treated wastewater. The obtained materials were characterized using Fourier-transform infrared spectroscopy (FTIR-ATR), X-ray diffraction (XRD), nitrogen adsorption-desorption measurements (BET), Raman spectroscopy, scanning electron microscopy (SEM), and UV-Vis spectroscopy to evaluate their chemical structure, crystallinity, textural properties, morphology, and optical characteristics. Photocatalytic experiments were performed under UV irradiation using real wastewater spiked with carbamazepine, diclofenac, ibuprofen, and sulfamethoxazole at an initial concentration of 50 mg/L, selected to ensure reliable quantification under laboratory conditions. The complete removal of diclofenac and sulfamethoxazole was achieved within 30 min of treatment, while the presence of the nanocomposite enhanced the degradation efficiency of ibuprofen and carbamazepine by approximately 19% and 13%, respectively, compared to UV irradiation alone. The obtained results demonstrate the applicability of the investigated g-C3N4/rGO system for pharmaceutical degradation in real wastewater matrices and indicate its potential as a preliminary photocatalytic post-treatment approach.

Lubricants are indispensable in tableting, as their impact extends beyond facilitating ejection to altering compressibility and tablet strength. However, the extent to which lubrication affects in-die compressibility, and whether these effects can be exploited for tensile strength modeling, remains insufficiently understood. This study systematically investigated the effects of lubricant type (magnesium stearate, sodium stearyl fumarate), concentration, and blending time on microcrystalline cellulose, dicalcium phosphate anhydrous, and an ibuprofen-based formulation using compaction simulation and compressibility analysis. Regression analysis confirmed that lubricant effects were expressed in the early compression phase. A nonlinear white-box model, based on tablet mass and onset density, accurately modeled tensile strength with mean squared errors ≤ 0.20 MPa across all materials and settings within the investigated dataset. These findings demonstrate that lubrication-driven variations in tabletability are inherently captured in compressibility data, enabling robust characterization of tensile strength without explicit lubricant information. Beyond model development, this work advances the understanding of lubrication effects in compressibility-based modelling and highlights its potential as a formulation-independent strategy for tabletability assessment within quality-by-design frameworks.

Moderate post-operative pain after orthopaedic surgery remains highly prevalent and frequently persists beyond hospital discharge, negatively impacting mobilisation, rehabilitation, patient satisfaction and risk of chronic post-surgical pain. Despite established multimodal analgesic strategies, clinical pathways remain heterogeneous and insufficiently standardised. The objective was to develop a shared, reproducible, and implementable model for the management of moderate post-operative pain in orthopaedic surgery, both during hospitalisation and at discharge, to support institutional protocol development. An adapted virtual Nominal Group Technique (vNGT) was used as a structured consensus methodology. Three regional expert panels were convened in Campania, Lombardia and Emilia Romagna. Before each meeting, panellists completed a structured pre-meeting survey and received preparatory material. Each meeting followed pre-defined vNGT stages: introduction and framing of the consensus questions, individual idea generation, structured round-robin sharing, clarification and refinement of statements, completion of standardised decision tables and post-meeting review/acceptance of the synthesised framework. Survey responses and decision tables were analysed qualitatively and comparatively to identify convergent principles, setting-specific operational variables, and reproducible components. A reproducible model emerged based on systematic pain assessment, defined analgesic targets, multimodal analgesia centred on scheduled non-opioid therapy, integration of loco-regional techniques, rational, rescue based opioid use, structured continuity of care at discharge. Setting specific declinations were defined for: (1) Day surgery and Arthroscopy, (2) major trauma surgery in the elderly, (3) lower limb arthroplasty surgery and (4) discharge management across all settings. This vNGT-derived framework provides an implementable structure for institutional pain management protocols in orthopaedic surgery and represents a foundation for future outcome-based validation studies.