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Background Introduction of Bare Metal Stents (BMS) was itself a revolutionary step in the history of the medical industry; however, Drug Eluting Stents (DES) maintained its superiority over BMS in every aspect from restenosis rate to late lumen loss. The reason behind the magnanimous position of the DES in the stent market is the degree of improvement with which it evolves. New and better stents come into the market every year, surpassing their predecessors by many folds. Literature review This review paper discusses the journey of DES with supporting clinical trials in detail. In the first generation, there were stainless-steel stents with thicker coatings. Although they had superior results compared to BMS, there was still room for improvement. Afterward came the second-generation stents, which had superior metal platforms with thinner struts and thin coatings. The drugs were also changed from Paclitaxel and Sirolimus to Zotrolimus and Everolimus. These stents performed best; however, there was an issue of permanent coating, which remained intact over the stent surface after complete drug elution and started to cause issues in longer-term studies. Hence, an improved version of DES was introduced to these permanent coatings called the third generation of drug eluting stents, which initially utilized biodegradable polymer and ultimately moved towards polymer free drug coatings. This generation has introduced a unique amalgam of technologies to achieve its polymer free coatings; however, researchers have numerous prospects of growth in this field. This review paper highlights the major coups of stent technology evolution from BMS to DES, from thick polymeric coatings to thin coatings and from durable polymers to polymer free DES. Conclusion In conclusion, though the medical industry promptly accepted BMS as the best treatment option for cardiovascular diseases; however, DES has provided even better results than BMS. In DES, the first and second generation has ruled the technology for many years and are still on the shelves. Still, the issues aroused due to durable polymer shifted the attention towards biodegradable drug eluting stents, the third generation growing rapidly. But the scientific community has not restricted themselves and is investigating bioresorbable stents that completely eliminate the polymer intervention in drug eluting stent technology.

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Our objective was to describe HIV/STD risk behaviours and awareness among a community-based sample of drug users in Pakistan. Drug users contacted through street outreach by a non-governmental organization in Quetta, Peshawar and Rawalpindi underwent interviewer-administered questionnaires. Descriptive statistics were used to characterize sexual behaviours by city, marital status and the use of injection drugs. Logistic regression was used to identify correlates of ever having an STD. Of 608 drug users studied, all but one was male; median age was 32 years and 45% had no formal education. Half were married, of whom 25% were living with their wives. Sexual behaviours were reported as follows: 14% had sex with other males, 28% reported sex with both males and females, 49% had paid money to have sex and only 10% had ever used condoms. One-fifth reported having had an STD and about 40% reported having suffered from either one or more STD-related symptoms. Only 41% had heard about HIV/AIDS, of whom 17% knew that HIV/AIDS could be transmitted through sexual contact. In conclusion, high-risk sexual behaviours are prevalent among male drug users in Pakistan, and awareness of transmission risks is low. These data attest to the urgent need for effective and specific interventions in Pakistan to prevent transmission of HIV and STDs among drug users and their sex partners.

Chiral Metal-Organic Frameworks (CMOFs) are unique crystalline and porous class of materials which is composed of organic linkers and metal ions. CMOFs surpass traditional organic and inorganic porous materials because of their tunable shape, size, functional diversity, and selectivity. Specific applications of CMOFs may be exploited by introducing desired functional groups. CMOFs have chiral recognition abilities, making them unique for chiral compound synthesis and separation. The CMOFs can be synthesized through different approaches. Two main approaches have been discussed, i.e., direct and indirect synthesis. Synthetic strategies play an essential role in getting desired properties in MOFs. CMOFs find potential applications in adsorption, asymmetric catalysis, luminescence, degradation, and enantioselective separation. The MOFs’ porosity, stability, and reusability make them an attractive material for these applications. The plethora of applications of CMOFs have motivated chemists to synthesize novel MOFs and number of MOFs have been ever-escalating. Herein, the synthetic methods of CMOFs and their various applications have been discussed.

This is the first epidemiological survey of mental disorders among children in a Malaysian village. The WHO Research Questionnaire for Children (RQC) was used for initial screening followed by a semistructured interview, the Follow-up Interview for Children (FIC) at its second stage. Diagnosis was obtained by using Rutter's multi-axial classification. The prevalence rates obtained for the diagnostic categories were described in 3 age groups, in accordance with the age of attending school in Malaysia. Of 507 children screened aged 1-15, 6.1% were classified as having a mental disorder.

Purpose The purpose of this paper is to investigate how corporate cash holdings changes across firm life cycle and how firms undergo heterogeneous dynamic cash adjustment as they advance from one stage to the next stage. Design/methodology/approach This study uses an extensive data set of 2,994 Chinese A-listed firms. The authors use generalized method of moments (GMM) and Fisher Panel unit root testing to investigate the targeting behavior of Chinese firms. Findings The uni-variate investigation reveals that firms in the growth stage exhibits the highest cash levels and firms in the decline stage report the lowest cash levels. As growth firms have high investment needs, they may require raising external capital to meet investment needs. To avoid the costly external financing, firms in growth stage tend to hold more cash. The GMM estimation reveals that along all the phases of firm life cycle there are evidences of trade-off behavior of corporate cash holdings. The authors report that adjustment rate increases as firms enters into the growth stage. Practical implications The findings provide both theoretical and practical insight to align cash policies with the available strategic choices along firm life cycle in an emerging market characterized by market imperfections. Originality/value The study is unique from the context that it is applying robust methodology to one of rarely investigated area in corporate cash policy. The peculiar Chinese study setting characterized by higher information asymmetry, high cost of external financing and heterogeneous access to financing sources provide theoretical and empirical underpinnings to investigate and gain insight about how corporate cash policy can be aligned with strategic choices available across different stages of life cycle.

This paper aims to develop a machine learning and deep learning-based real-time framework for detecting and recognizing human faces in closed-circuit television (CCTV) images. The traditional CCTV system needs a human for 24/7 monitoring, which is costly and insufficient. The automatic recognition system of faces in CCTV images with minimum human intervention and reduced cost can help many organizations, such as law enforcement, identifying the suspects, missing people, and people entering a restricted territory. However, image-based recognition has many issues, such as scaling, rotation, cluttered backgrounds, and variation in light intensity. This paper aims to develop a CCTV image-based human face recognition system using different techniques for feature extraction and face recognition. The proposed system includes image acquisition from CCTV, image preprocessing, face detection, localization, extraction from the acquired images, and recognition. We use two feature extraction algorithms, principal component analysis (PCA) and convolutional neural network (CNN). We use and compare the performance of the algorithms K-nearest neighbor (KNN), decision tree, random forest, and CNN. The recognition is done by applying these techniques to the dataset with more than 40K acquired real-time images at different settings such as light level, rotation, and scaling for simulation and performance evaluation. Finally, we recognized faces with a minimum computing time and an accuracy of more than 90%.