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The integration of smartphones with optical nanoprobes has emerged as a powerful approach for developing portable and cost-effective analytical platforms. Paper-based microfluidic analytical devices (µPADs) and microfluidic chips further enhance the practicality of smartphone-integrated optical sensors. These portable sensors enables on-site, and point-of-care testing offering a sustainable and green alternative to conventional analytical methods by minimizing energy consumption, reducing analysis time and cost, and eliminating the need for sophisticated equipment. This review explores various types of optical nanosensors, including plasmonic nanoparticles, quantum dots, metal-organic frameworks (MOFs), upconversion nanoparticles, and carbon quantum dots (CQDs), highlighting their unique optical properties. The review also discusses different detection methods for these sensors such as colorimetric, fluorescence, and ratiometric fluorescence assays, emphasizing their role in enhancing sensitivity and selectivity. Additionally, the integration of machine learning algorithms in nanosensor analysis is explored, demonstrating its potential for handling complex data and improving detection performance. The review highlights key applications in biosensing, heavy metal detection, and food contaminant analysis while addressing critical challenges such as reproducibility, imaging optimization, and data processing. Overcoming these challenges will be crucial for the widespread adoption of smartphone-integrated optical nanoprobes in real-world applications.

Protracted conflict has exacerbated existing drivers of antimicrobial resistance in Syria and introduced new ones; these drivers intersect, leading to a rising threat across Syria’s health system, where untreatable infections are rife. To tackle AMR in Syria, coordinated, multisectoral approaches which define the governance, behavioural and contextual factors, and the measures required to address them are needed. These require high-level leadership and investment from all vested stakeholders.

Primary and community health care must be the backbone of the health system to support equitable population coverage, 10 particularly in rural areas and for the most vulnerable populations, including children, people living with disabilities, internally displaced people, returnees, and released detainees. 11,12 Investment is needed in health-care workforce production, distribution, and training, including recognition of graduates from unaccredited universities and their equitable disbursement. To support this approach, community-oriented training and cross-sector collaboration will be crucial to strengthen primary care, health promotion, and disease prevention. 13,14 Aris Messinis/AFP via Getty Images The main public health burdens that have been exacerbated by the conflict include maternal, child, and neonatal health, malnutrition, mental health, and non-communicable diseases; 15 these require multimodal models of care to support diagnosis, access to services, and management. Immunisation strategies should take into account the continued mobility of the population for both internally displaced people and returnees. 19 Additionally, addressing relevant infrastructure, such as water and sanitation, electricity, and shelter, is key to disease prevention. 20 To address these public health priorities, the reformed health system must prioritise equity and governance, ensuring care is based on needs, not on top-down political decisions—a shift away from the “punishment by neglect” approach implemented by the former Assad regime. 21 There must also be a transitional justice process that includes all sectors and professionals (including employees previously dismissed by the Assad regime for political reasons), and the development of a human resources plan (in coordination with the Ministry of Social Affairs and Labour) to ensure equitable distribution of resources and personnel.

Pure component contribution algorithm (PCCA) is an accurate analytical method was developed for extracting the contribution of each component from a mixture where the components of this mixture are completely overlapped. It is based on elimination the signal of interfering components using mean centering as a processing tool finally, the pure contribution of each component is extracted form complex signals and that worked as a finger-print resolution for the component. (PCCA) has been successfully applied to UV data of ciprofloxacin hydrochloride (CIP) in the presence of its acidic degradation product (DCIP). The spectrophotometric technique (PCCA) was successfully applied to the laboratory prepared mixtures and the pharmaceutical dosage form where the purity of the extracted signals were tested by calculating the spectral contrast angle (0) where the results were compared to show the capability to recover pure spectral profiles and detect the presence of impurities. The proposed method proved that spectrophotometric techniques can be used for identification and separation of signals, similar to chromatographic techniques. The method validated according to the ICH guidelines and the accuracy, precision and repeatability found to be within the acceptable limits. The selectivity of the proposed method was tested using laboratory prepared mixtures and assessed by applying the standard addition technique. So, they can be used for the routine analysis of ciprofloxacin in quality-control laboratories.