Lalit Kumar

10.3k citations
430 papers · 6.9k · 1 hit paper · h-index 42

Impact in

Papers in

Lalit Kumar

391 papers receiving 6.7k citations

Lalit Kumar's Hit Papers

3D Printing as a Promising Tool in Personalized Medicine 2021 · 293 citations
2930+1+3Years since publication50100150200250

Peers

Lalit Kumar
Comparison fields: 5 of 192
  • Pharmaceutical Science 803
  • Cancer Research 597
  • Hematology 415
  • Biomaterials 346
  • Genetics 248
Replace Ge Zhang with:
Ge Zhang China
Lei Nie China
Guohui Liu China
Jeong Hun Kim South Korea
Ben Wang China
Giovanni Traverso United States
Shuang Zhao China
Xuemei Zhang China
Bengt Fadeel Sweden
Yan Wang China
Lalit Kumar relative to Ge Zhang China Ge Zhang's profile →
Citations per field
00.5×1.5×2.1×
Ge Zhang · 1×
Citations per year

Countries citing papers authored by Lalit Kumar

Since Specialization
Citations

This map shows the geographic impact of Lalit Kumar's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Lalit Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lalit Kumar more than expected).

Fields of papers citing papers by Lalit Kumar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lalit Kumar. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Lalit Kumar. The network helps show where Lalit Kumar may publish in the future.

Co-authors

The 25 scholars most cited alongside Lalit Kumar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Lalit Kumar Line = papers co-authored together Lalit Kumar links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 430 papers — load more, or switch the sort, to bring in the rest.

#Work
1
3D Printing as a Promising Tool in Personalized Medicine
Hit paper breakdown →
2021293
2 2019216
3 2016161
4 2018122
5 2010120
6 2022112
7 2010106
8 2015104
9 2023100
10 199996
11 201696
12 201491
13 201888
14 197587
15 201985
16 201678
17 201576
18 201875
19 201873
20 200970

About Lalit Kumar

Lalit Kumar is a scholar working on Molecular Biology, Pharmaceutical Science, Pathology and Forensic Medicine, Oncology and Hematology, having authored 430 papers that have together received 6.9k indexed citations. Recurring topics across this work include Lymphoma Diagnosis and Treatment (40 papers), Advanced Drug Delivery Systems (36 papers), Analytical Methods in Pharmaceuticals (32 papers), Drug Solubulity and Delivery Systems (29 papers), Advancements in Transdermal Drug Delivery (29 papers), Chronic Lymphocytic Leukemia Research (25 papers), Multiple Myeloma Research and Treatments (23 papers) and Analytical Chemistry and Chromatography (17 papers). The work is most often cited by research in Pharmaceutical Science (803 citations), Cancer Research (597 citations), Hematology (415 citations), Biomaterials (346 citations) and Genetics (248 citations). Lalit Kumar has collaborated with scholars based in India, United States and Australia. Frequent co-authors include Ruchi Verma, Shivam Prakash Gautam, Shubhrata Gupta, Rupesh Kalidas Shirodkar, Shamsuzzama Shamsuzzama, Salwa, Aamir Nazir, Rizwanul Haque, Navya Ajitkumar Bhaskaran and Atul Sharma. Their work appears in journals such as Journal of Clinical Oncology, Annals of Hematology, Journal of Drug Delivery Science and Technology, European Journal of Medicinal Chemistry and Journal of Pharmaceutical Innovation.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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